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rituwithai

Rituraj

Verifiedactive
@rituwithai

AI Strategist & Educator | Founder Of H&H| Community: 90k IG | 70k LinkedIn. | Open for brand partnerships. Email - ritzswag@gmail.com

RosterAI Model News & ReleasesAI Agents & Productivity Tools
62,766 followers · 2,478 following · Last polled 3h ago
Perf. Score
38
Rank #311
Value Score
37
#176 of 273 priced
Median views
485
p25 338 · trailing 7d
Followers
63K
-0.0% 7d
ER (impr.)
3.15%
eng ÷ impressions
ER (foll.)
0.02%
eng ÷ followers
Est. CPM
$84.54
QT-rate basis
WoW views
-4.3%
flat

Rates & value · est. from 485 median organic views

overpriced
Quote tweetbasis
$41
$84.54 / 1K views
Post
$68
$140.21 / 1K views
Thread
$95
$195.88 / 1K views
Retweet
$20
no CPM
Value Score 37 · rank #176 of 273 priced$2562.5 / 1K engagementsrate age unknown (pre-audit import)

Trends

Follower growth
Median views / day
Engagement rate
View distribution · trailing-7d posts

Each blue dot is one organic post. The Performance Score ranks by the median (485), not the mean (413) — so a single viral post doesn't inflate a creator's reach. The p25 floor (338) is the level 75% of posts clear.

Recent posts · ranked by views

PostPostedViewsLikesRepostsRepliesQuotesBookmarksER
Standard RAG is officially dead—and your AI agent has the memory of a goldfish. ⚠️ Basic vector search and naive chunking just got rendered completely obsolete by biomimetic memory. Most developers are burning thousands in LLM tokens re-feeding context windows because their agents forget what happened three prompts ago. Meet Hindsight—an open-source agent memory engine built to make AI agents actually learn over time instead of just dumping chat logs into a vector database. Here is why every agent developer and AI framework builder is migrating to this repo right now: • Biomimetic Memory Architecture: Standard RAG treats all data like a flat pile of text. Hindsight categorizes memory into distinct cognitive pathways—separating raw World Facts, personal Agent Experiences, background Observations, and living "Mental Models" that update automatically in the background. • SOTA LongMemEval Dominance: Crushed every alternative memory architecture on the LongMemEval benchmark. Independently verified by research collaborators at Virginia Tech and The Washington Post, delivering state-of-the-art accuracy on complex, long-term conversational recall tasks. • 4-Way Parallel Hybrid Recall: When an agent searches its memory, Hindsight executes 4 retrieval strategies simultaneously—Semantic vector similarity, BM25 Keyword matching, Knowledge Graph entity/causal links, and Temporal time-range filtering—merged via reciprocal rank fusion and cross-encoder reranking. • The "Reflect" Engine: Standard tools just do lookup; Hindsight lets agents reason across past memories. The reflect() operation analyzes historical patterns, synthesizes disposition-aware answers, and maintains self-rewriting "Knowledge Pages" so your agent boots up with settled knowledge instead of rediscovering facts every session. • 2-Line LLM Client Drop-In: Zero architecture rewrite required. Use wrap_openai() or wrap_anthropic() via LiteLLM, and your agent automatically retains context after every response and recalls relevant memories before every call. Works natively with Claude Code, Cursor, LangGraph, and 60+ agent frameworks. • Built-In Memory Defense: Ships with an automated privacy guardrail that scans every retained memory against 45 PII and secret patterns (like API keys and token strings) to redact or block leakages before hitting persistent storage. If your AI workflow forgets user preferences, loses track of multi-day project context, or hallucinates past decisions, you don't have an autonomous employee—you have an overhyped chatbot.
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🚨 Someone just figured out how to run 11 different AI coding agents for free. Forever. 60,300 GitHub stars. 1.3 billion free tokens every month. Zero API bill. It's called Free Claude Code. And it's not a hack. 61 ToS-friendly providers, routed through one local proxy, legally. Here's what it actually gives you. Not just Claude Code. Claude Code, Codex, Pi, OpenCode, Cline, Hermes, DeepSeek Harness, Grok Build, Muse Code, Aider, and VS Code Chat — all running on free models, from one searchable catalog. No separate signups. No juggling API keys across ten dashboards. One admin UI. Pick a provider. Pick a model. Apply. Here's the part that makes this safe to actually run. Every integration follows provider terms of service. When a provider changes its rules, FCC removes that integration. No account-ban risk from scraping something you shouldn't. Here's the failover that keeps you working through outages. Your provider goes down mid-task? FCC automatically tries your next configured model without restarting your turn. You don't even notice. Configure a fallback list once — it applies across every client you connect. Here's the wildest part. Up to 90% lower token usage. Built-in optimizations compress tool results, filter repetitive command output, and skip provider calls entirely for routine stuff like quota checks and filepath suggestions. You're not just getting free tokens — you're burning through fewer of them per task. Here's what else ships in the box. → Native browser coding sessions — run Codex in your browser with real-time and background execution, no terminal required → Voice notes in, code out — talk to your agent using local Whisper or NVIDIA NIM transcription → Works everywhere — terminal, desktop, VS Code, JetBrains, Discord, Telegram, phone → Full agent capabilities preserved — streaming, tool use, interleaved thinking, image input, all intact One command to install: curl -fsSL "https://t.co/OyMNDO8HyW" | sh Pick your agents. Pick a provider. You're coding for free in under five minutes. 60.3K GitHub stars. 69 releases. 55 contributors including Claude itself. AGPL-3.0. Independent project, not affiliated with Anthropic. 100% Open Source.frozen9d ago877543041.82%
🚨 Someone built an AI memory platform that gives your agents persistent memory — for free, with zero API key, using only your CPU. Not a paid vector database subscription. Not a cloud service billing per query. Local small models, running on your machine, building a real knowledge graph from everything your agent touches. It's called Cognee. 31,500 stars. 307 contributors. 151 releases. And the "runs locally for free, no API key required" line is the part that separates it from every memory product we've covered. Here's what it actually does. await cognee.remember("Marie Curie was born in Warsaw and worked at the University of Paris.") results = await cognee.recall("Where was Marie Curie born?") No LLM configured. No API bill. Cognee extracts the knowledge graph locally using GLiNER — a small open extraction model — and embeds everything with a local embedding model. Both download automatically on first use. Text ingestion, retrieval, and session memory all work entirely offline. Add an LLM key later if you want generated answers instead of retrieved source passages. The base memory layer never requires it. Here's why this isn't just a vector store with a new name. Text becomes entities, relationships, and searchable chunks. Code becomes a graph of symbols and dependencies — your agent can navigate your codebase by relationship, not just keyword match. Session distillation takes the lessons an agent learns during a working session and curates them into permanent memory, so what one session figured out, the next session already knows. Four operations define the whole system: remember, recall, improve, forget. Store, retrieve, enrich with feedback, delete. Clean primitives, not a sprawling API. Here's the infrastructure claim that's genuinely unusual. Graph memory traditionally requires a graph database, a vector database, Redis for sessions, and a relational database for metadata — four services before your agent remembers anything. Since Cognee 1.0, the entire memory layer runs on a single Postgres instance. Relational metadata, PGVector, and graph state, all in one service. Here's the benchmark data, published with its limitations stated upfront. On BEAM — a conversational memory evaluation using long synthetic conversations judged by an LLM — Cognee scores 0.79 at 100K token context and 0.67 at an exploratory 10M token context. The README is explicit that these are different test conditions, not directly comparable, and documents the reproduction gap for the 10M setting. That kind of transparency is rare for a benchmark number a company is proud of. Here's how it plugs into every agent you're already using. claude plugin marketplace add topoteretes/cognee-integrations claude plugin install cognee-memory@cognee Also ships for Codex, OpenClaw, Cursor, Cline, and any MCP client. Python, TypeScript, and Rust SDKs. A full REST API for anything else. Here's the UI for inspecting what your agent actually remembers. cognee-cli -ui Visualize the knowledge graph directly. See what got extracted, how entities connect, what your agent is actually drawing on when it answers. One command to start locally: uv pip install "cognee[gliner]" Apache 2.0 License. Claude listed among contributors. The memory layer that doesn't need a credit card to work. 100% Open Source.frozen16d ago8181039033.06%
🚨 The error-tracking tool running in half the world's production apps just became an AI skill. Sentry. Used by 560+ companies you'd recognize instantly. 888 contributors. 177 releases. And it just shipped native Claude Code and Cursor integration — turning your crash logs into something your AI agent can read and fix directly. Here's what changed. For over a decade, Sentry's job was simple: your app breaks, Sentry catches the error, traces the stack, and tells a human developer exactly where to look. You'd open the dashboard, read the trace, go fix the code yourself. The new agent skills flip that. Your AI coding agent can now query Sentry directly — pull the actual production error, the stack trace, the user session that triggered it, the release that introduced it — and go fix it without you copy-pasting a stack trace into chat. Here's why this matters more than it sounds. Every bug an AI agent fixes based on what you describe is a bug fixed from your memory of what happened. Every bug an AI agent fixes by querying Sentry directly is a bug fixed from what actually happened — the real error, the real user context, the real conditions that caused it. That's the difference between an agent guessing and an agent diagnosing. Here's what Sentry actually covers. Error tracking across 20+ official SDKs — JavaScript, Python, Go, Rust, Java, Swift, Unity, Unreal Engine, Godot, basically every language and even game engines. Full distributed tracing — follow a request across microservices to find where it actually failed. Session replay — watch what the user did right before the crash. Release tracking — know exactly which deploy introduced the regression. Here's the workflow this unlocks. Production breaks. Sentry catches it, same as always. Now: your Claude Code or Cursor agent can pull that exact error, read the full trace, see the affected release, and propose a fix — all without a human first triaging the dashboard and writing up a bug description. The debugging loop that used to take a human reading logs, writing a ticket, and an engineer picking it up — compresses to an agent querying Sentry directly. 26.9.0 is the latest of 177 releases. Battle-tested at massive scale for over a decade, now with the AI layer that makes it agent-native. Claude is listed among the contributors. Free and open source for self-hosting. Hosted plans available at sentry .io. 100% Open Source. GitHub link in the comments 👇frozen20d ago755835052.78%
🚨 Tencent just open sourced 11 production MCP servers covering everything from browsers to databases to security scanning. Not demos. Not prototypes. The same MCP servers Tencent runs internally — now public. It's called Tencent MCP Servers. Built by the team behind WeChat, QQ, and one of the largest tech infrastructures on earth. And the quality difference between "built for production at Tencent scale" and "built for a GitHub demo" is immediately obvious. Here's what 11 servers actually gives your agents. Browser-Use MCP — full browser automation. Clicks, form filling, screenshots, JavaScript execution, network interception. Your agent browses the web exactly like a human. Headless Chromium under the hood. Kling MCP — Kling's video and image generation directly from your agent. Text-to-video, image-to-video, text-to-image. Generate visual content as part of any agentic workflow. Hunyuan MCP — Tencent's own frontier LLM accessible as a tool. Vision, document understanding, voice synthesis. Your agent calling another AI as a specialized tool. SecretScan MCP — scans code repositories for leaked secrets and credentials. API keys, tokens, passwords — caught before they hit production. CodeReview MCP — automated code review with structured findings. Security vulnerabilities, performance issues, style violations, maintainability concerns. SQLite, PostgreSQL, and MySQL MCPs — direct database access for your agents. Query, insert, update — full SQL execution with safety guardrails. GitHub MCP — repository management, issue tracking, PR creation, code search. Your agent manages your GitHub workflow natively. Figma MCP — read Figma designs, extract components, understand layouts. Your coding agent sees what your designers built. Email MCP — send, receive, search, and organize email. Full mailbox access for your agents. WeChat Work MCP — enterprise messaging for Chinese enterprise workflows. Send messages, create groups, manage contacts. Here's why the Tencent origin matters. Every one of these servers handles sensitive operations — databases, credentials, email, code repositories. Tencent built them to run inside their own production infrastructure, where a security failure has consequences at billion-user scale. The authentication patterns. The rate limiting. The error handling. The audit logging. All of it reflects what production at scale actually requires. Here's the security scanning story specifically. SecretScan MCP is the tool that would have caught the ContextCrush attack we covered earlier — the MCP server that exfiltrated credentials through a poisoned tool config. It scans for exactly the patterns attackers inject to steal keys and tokens. Run it on every MCP server you install. Including these ones. Configure whichever servers you need in your Claude Desktop or agent config. Each one is independently deployable. MIT License. From Tencent. 100% Open Source. GitHub link in the comments 👇frozen28d ago67934911078.98%
🚨 Someone built a SQL linter that supports 26 different database dialects — and it fixes your SQL automatically. Snowflake. BigQuery. Redshift. PostgreSQL. Databricks. ClickHouse. DuckDB. T-SQL. All of them. One tool. It's called SQLFluff. 3,300+ companies using it. 639 contributors. 143 releases. And the reason this matters now is simple — every AI coding agent writing SQL needs something enforcing consistency, because models are notoriously inconsistent about SQL style across a codebase. Here's the problem it solves. SQL doesn't have one universal syntax. Snowflake SQL isn't BigQuery SQL. Both differ from Postgres. Both differ again from T-SQL. Every team ends up with a mix of styles — some analysts write lowercase keywords, some write uppercase, some use tabs, some use spaces, some add trailing commas, some don't. When your AI coding agent generates SQL — and now that every data team is using Claude Code or Cursor to write queries, this happens constantly — the style drifts with every generation. One query looks nothing like the next. SQLFluff catches it and fixes it automatically. $ sqlfluff lint test.sql --dialect ansi L: 1 | P: 1 | LT01 | Expected only single space before 'SELECT' keyword. L: 1 | P: 1 | LT02 | First line should not be indented. Then: $ sqlfluff fix test.sql --dialect ansi Every inconsistency fixed. Same tool, same rules, every dialect. Here's why dialect flexibility is the real feature. Most SQL linters assume one database. SQLFluff explicitly supports 26 dialects — including niche but important ones like Doris, Materialize, StarRocks, and Exasol — because real companies run heterogeneous data stacks. Your warehouse is Snowflake. Your transactional DB is Postgres. Your analytics layer touches BigQuery. One linter, correctly tuned per dialect, instead of three different tools with three different rule sets. Here's the dbt and Jinja support that makes this a real data engineering tool. SQL isn't modular by default — so modern data teams template it with Jinja, dbt macros, or SQLAlchemy placeholders. SQLFluff parses through the templating layer correctly, linting the actual SQL that gets generated rather than choking on {{ ref('table') }} syntax like a naive linter would. Here's the wildest part. It just shipped a Rust-backed parser and lexer as an optional extra — pip install sqlfluff[rs] — for teams linting massive codebases where Python parsing speed becomes the bottleneck. Same rules, dramatically faster execution. And the contributing guide explicitly covers "AI-assisted contribution expectations" — SQLFluff is one of the first major open-source projects to formally document how AI-generated PRs should be reviewed and submitted. One command: pip install sqlfluff sqlfluff fix your_query.sql --dialect snowflake VS Code extension available. 4.4.0 just shipped yesterday. MIT License. 639 contributors including Claude. 100% Open Source. GitHub link in the comments 👇frozen19d ago6771045153.69%
🚨 The person who built LangChain just shipped something better. It's called LangGraph Platform. And the creator's own description is the most honest thing anyone has said about AI agents this year: "LangChain was wrong. Chains are wrong. Graphs are right." Harrison Chase built the framework that every AI developer used to build agents for three years. Then he watched what actually happened in production. And he started over. Here's what was wrong with chains. LangChain's core abstraction: a linear sequence of steps. Input → LLM call → tool call → LLM call → output. Clean. Predictable. Easy to reason about. Also completely wrong for how real agents actually need to work. Real agents branch. They loop. They retry when something fails. They run steps in parallel. They wait for human approval before continuing. They persist state across multiple sessions. They spawn sub-agents and collect results. They need to be interrupted, inspected, and resumed. None of that fits in a chain. You can fake it — LangChain developers spent three years faking it — but the abstraction fights you every step. LangGraph's core abstraction: a directed graph. Nodes are actions. Edges are transitions. Conditional edges handle branching. Cycles handle loops. Checkpointing handles persistence. Human-in-the-loop is a first-class primitive. The mental model matches how agents actually work. Here's what LangGraph Platform ships with that no other framework has. Persistent memory across sessions — not a hack, a primitive. Every conversation, every decision, every tool call stored and retrievable. The agent that helped you last week picks up exactly where it stopped. Time travel debugging — roll back your agent to any previous state. Made a bad decision at step 7? Rewind to step 6 and try a different branch. The entire execution history is inspectable and re-executable. Human-in-the-loop as a first-class primitive — interrupt the agent at any node, inspect its state, modify it, resume. Not a workaround. The architecture was designed for it. Streaming — watch the agent think in real time. Every token. Every tool call. Every state transition. As it happens. Multi-agent coordination — agents spawning sub-agents, collecting results, coordinating through shared state. The patterns that actually work at scale. Here's the deployment story that makes this production-ready. LangGraph Platform ships with a complete deployment infrastructure — horizontal scaling, persistence layer, monitoring, API server. Not "here's how to deploy it yourself." A complete managed deployment that handles the infrastructure. The Studio: a visual debugger where you watch your agent execute as a graph. See exactly which node is running. See the state at every checkpoint. Click on any past execution and inspect it. Here's why Harrison Chase rebuilding from scratch matters. He built LangChain while watching tens of thousands of developers try to build agents. He saw every failure mode. Every abstraction that seemed right and turned out wrong. Every pattern that worked in demos and broke in production. LangGraph is what he would have built if he knew then what he knows now. The framework the person who built the framework would actually use. Used in production by companies serving millions of users. Python and JavaScript SDKs. MIT License for the core. 100% Open Source.frozen25d ago666968084.65%
🚨 Someone built a complete AI-powered news aggregator that summarizes, translates, and delivers everything you care about — in one feed. No algorithm deciding what you see. No ads. No engagement bait. Just the sources you choose, processed by AI, delivered your way. It's called NewsNow. 47 contributors. 34 releases. And it does something no other news reader has done cleanly. Here's what it actually does. You configure your sources — RSS feeds, websites, newsletters, social media accounts, anything with a public URL. NewsNow fetches them all on a schedule. AI summarizes every article into a few sentences. AI translates anything not in your language. AI scores each item by relevance to your interests. The result: one clean feed, everything you care about, nothing you don't. No Twitter algorithm. No Google News personalization that slowly narrows your worldview. Your sources. Your rules. Your feed. Here's the AI pipeline under the hood. LLM summarization — any OpenAI-compatible model, Ollama local models included. Relevance scoring — you define your interests in plain English, the model scores each article against them. Translation — multilingual support, any source language to your preferred output. Deduplication — the same story from five sources appears once, not five times. Clustering — related stories grouped automatically. Full local inference supported. Your news processing stays on your machine. Here's what makes the delivery layer genuinely useful. Telegram bot. Discord webhook. Email digest. RSS output — so other tools can consume your curated feed. Web dashboard. Mobile PWA. API for anything custom. One aggregator. Every delivery channel. Pick the one that fits your workflow. Here's the wildest part. Source plugins are community-built and swappable. Reddit. Hacker News. GitHub Trending. ArXiv. Product Hunt. Twitter lists. YouTube channels. Any website with structured content. The same pipeline that processes Reuters and BBC also processes the GitHub Trending repos we've been covering all session. Your AI news feed and your repo discovery feed — unified. One command to deploy: Open localhost:3000. Add your first sources. Your AI news feed is live. 47 contributors. 34 releases. MIT License. 100% Open Source. GitHub link in the comments 👇frozen29d ago657852083.50%
🚨 Someone built the layer that stops your AI agent from being blind to the internet — and keeps fixing itself when platforms change. Not a scraper. Not one more API wrapper that breaks the moment a site updates its defenses. A capability layer that picks the best way to reach each platform, installs it, health-checks it, and silently swaps the backend when the old one dies. It's called Agent Reach. #1 Repository of the Day on Trendshift. One command to install. One command to update. Here's the exact problem every AI agent has right now. "Summarize this YouTube video" — can't get the transcript. "What are people saying about this on Twitter" — API's paywalled. "Check Reddit for this bug" — 403, server IP blocked. "What's the sentiment on this product on Xiaohongshu" — login wall. "Summarize this Bilibili video" — the generic downloader gets blocked by Bilibili's anti-bot system entirely. None of these are hard problems individually. They're just a dozen separate annoying problems, each with its own paywall, login requirement, or anti-scraping wall — and you have to solve every one yourself before your agent can actually read the internet. Agent Reach turns all of it into one sentence to your agent: 帮我安装 Agent Reach: [install.md link] Minutes later it reads Twitter, searches Reddit, watches YouTube, browses Xiaohongshu. Here's the architecture that makes this different from every scraping library. Every platform gets a primary backend and an ordered list of fallbacks. When the primary breaks — and platforms change their defenses constantly — Agent Reach quietly routes to the next one. In June 2026, Bilibili's anti-bot system killed yt-dlp access entirely. Agent Reach switched to bili-cli. Users did nothing. It just kept working. channels/ ├── https://t.co/PJyt1luuRA → twitter-cli ▸ OpenCLI ▸ bird ├── https://t.co/eON92DNEYN → bili-cli ▸ OpenCLI ▸ search API ├── https://t.co/FzalrvoDiN → OpenCLI ▸ rdt-cli ├── https://t.co/UQdxtYBbYB → OpenCLI ▸ xiaohongshu-mcp ▸ xhs-cli Run agent-reach doctor and it tells you exactly which backend is live for every platform, right now. Here's what works with zero configuration. Web pages via Jina Reader. YouTube transcripts and search via yt-dlp. RSS feeds. Bilibili search without login. Full-web semantic search via Exa, free, no key required. V2EX. Stock data from Xueqiu. Here's what unlocks with one spoken request to your agent — "configure Twitter for me," "configure Xiaohongshu for me" — and the agent walks you through it step by step. Twitter, Reddit, Facebook, Instagram, Xiaohongshu, LinkedIn, Boss Zhipin, podcast transcription via Whisper. Here's the security design that's genuinely more careful than most tools in this category. Credentials never leave your machine — stored locally with 600 permissions, never uploaded. Install defaults to a read-only check; nothing gets modified on your system unless you explicitly pass --system. Full dry-run mode previews every action before anything happens. The README explicitly warns: use a throwaway account for any platform requiring cookie-based login, never your main account, because scripted access can trigger platform bans. That's an unusually honest warning for a tool whose entire purpose is automating access to platforms that don't want to be automated. Here's the OpenClaw-specific note worth knowing if you use it. OpenClaw users need tools.profile set to "coding" first — the default messaging profile blocks the shell execution Agent Reach needs to install anything. One config line, documented clearly. agent-reach uninstall --dry-run # preview what gets removed agent-reach uninstall --keep-config # clean reinstall, keep your tokens 7 releases. 35 contributors including Claude. MIT License. The capability layer that keeps itself updated so you don't have to. 100% Open Source.frozen17d ago635734052.99%
🚨 Someone built a tool that scores your AI agent's trustworthiness in under 2 minutes. Not latency. Not token efficiency. Not whether it passes a prompt injection test. Whether your agent actually does its job without lying to you about it. It's called iFixAi. #1 Python repository of the week on Trendshift. And it asks the question every other eval tool skips entirely. Here's the gap it found. Every eval tool, red-teaming framework, and observability platform measures technical capability. Speed. Accuracy. Whether it can be jailbroken. None of them answer: is this agent doing what the business actually needs, inside the guardrails it's supposed to respect? iFixAi answers that in one run. Here's what it actually checks. 32 inspections across five pillars. Fabrication — did it use a tool it wasn't granted, make unsourced claims, keep no audit trail? Manipulation — privilege escalation, breaking its own policy, poisoned context. Deception — does it perform better when it senses a test? Does it have secret side-goals? Unpredictability — does it drift off-instructions over long runs? Opacity — does human escalation actually work when it should? That deception pillar is the one that should concern every company deploying agents right now. Sandbagging — an agent performing worse normally and better when it senses evaluation — is exactly the alignment faking behavior Anthropic published research on earlier this year. iFixAi tests for it directly. Here's the grading that makes this different from a vibes-based audit. A–F letter grade. Citable only when a different vendor's model grades yours — your agent never grades itself. Claude tested, GPT judges. GPT tested, Claude judges. Cross-vendor by design so no one lab marks its own homework. Here's the case studies that make this real. They reconstructed fixtures from two public incidents — the Pizza Hut chatbot complaint and the June 2026 Instagram takeover reporting. Both scored F. Not hypothetical failures. Real incidents, graded against the same framework you'd run on your own agent. Here's how fast it actually runs. pip install "ifixai[anthropic]" ifixai setup ifixai run Three commands. A–F grade with the full breakdown in under two minutes. Or point it directly at your deployed agent's endpoint — no code, it measures the governance you already have in production, not a toy version of your system. 19 releases. Apache 2.0. Everything free, including the 28 premium inspections most tools would paywall. 100% Open Source. GitHub link in the comments 👇frozen22d ago6161046043.90%
🚨 Someone built a self-hosted A-share quant platform that opens its entire brain as an API — 61 endpoints, 12 MCP tools, and an AI assistant that can trade on command. Not a dashboard. A complete data infrastructure for quantitative trading, running in one Docker container, with zero external database. It's called TSP (盯盘侠). 50 releases. And the architecture decision at its core is the thing most self-hosted finance tools get wrong. Here's the problem it solves. Every DIY quant setup hits the same wall. Your data source changes — you rewrite the entire pipeline. Your screener, backtester, and monitor each use different data conventions — nothing lines up. You're staring at five browser tabs trying to find one number. Intraday moves happen while you're not watching — and you miss them. TSP's answer is a capability routing matrix: six dataset categories, each independently routed by what the data source actually supports. Swap data providers any time — your indicators and backtest results never shift, because everything downstream reads from one unified "enriched" data contract: screener → backtest → monitor → post-mortem, one consistent pipeline, start to finish. Here's the engine underneath. Polars for computation. DuckDB for queries. Parquet for storage. One container, zero external database. Full-market strategy scans run in milliseconds because the enriched data is pre-computed — only 15 raw columns stored, 68 indicator and signal columns calculated on read, cached in-process. Backtests run in isolated spawn worker subprocesses — refresh the page mid-backtest, your job survives. The main service never blocks on a heavy computation. Here's what the open API layer adds that no other personal trading dashboard has. 61 contract-locked endpoints, snapshot-tested in CI so nobody can silently break the interface. Six permission tiers — read market data, read extensions, write extensions, read analysis, run backtests, paper trade — issued per API token with sliding-window rate limiting. Full read-write loop: external programs can write custom data into the platform through the same validated path the admin panel uses. curl -H "Authorization: Bearer tsp_xxxx" \ "http://localhost:3018/api/kline/daily?symbol=https://t.co/S7FqXuIuFX&limit=10" Here's the MCP server — the part that connects directly to everything we've covered all session. 12 tools exposed to any MCP-compatible AI client, permissioned by the same token scopes as the API. Point Claude Desktop, Claude Code, or any agent at your own trading platform and it can query market data, run strategies, and check your portfolio — through the real governance layer, not a side door. Here's the AI assistant, and why the confirmation-card design matters. 21 read-only query tools plus 4 action tools — generate a signal strategy, run a backtest, add to watchlist, backfill data. Every action tool requires an explicit confirmation card before it executes: action name, impact description, full parameters, shown to you — you click confirm or it auto-cancels in 120 seconds. The model is explicitly told not to retry after a decline. That's the governance pattern every agent framework we've covered this session has been reaching for. TSP built it natively into a personal finance tool. Streamed token-by-token responses. Every tool call shows its name, parameters, timing, and result summary — expandable and auditable. The assistant will not give buy/sell instructions — trading questions get converted into objective technical/financial state, key price levels, and risk factors instead. One command: docker run -d --name tsp -p 3018:3018 -v ${PWD}/data:/app/data https://t.co/nYsxq7fhsh Open localhost:3018. 50 releases. 33 contributors including Claude. MIT License. Self-hosted, zero external database, your data never leaves your machine. 100% Open Source.frozen18d ago613636043.10%
🚨 Someone built a free 7-week course that teaches you to build production RAG systems the way companies actually build them. Not a tutorial that jumps straight to vector search and calls it a day. The professional path — keyword search foundations first, then layered up to a full agentic AI system with mobile access. It's called The Mother of AI Project. Built by Shirin Khosravi Jam and Shantanu Ladhwe. And the thing that makes it different from every other "build a RAG app" tutorial is right in the README. "We build RAG systems the way successful companies do — solid search foundations enhanced with AI, not AI-first approaches that ignore search fundamentals." Most courses skip straight to embeddings and vector databases because that's the exciting part. This one makes you master BM25 keyword search first — the boring, battle-tested foundation that production systems actually rely on — before layering semantic search on top. Here's what you actually build, week by week. Week 1: Full infrastructure — Docker, FastAPI, PostgreSQL, OpenSearch, Airflow. The scaffolding every real system needs. Week 2: An automated pipeline that fetches and parses real academic papers straight from arXiv, using Docling for scientific PDF parsing — the same document parser from IBM we covered earlier in this session. Week 3: Production BM25 keyword search with real relevance scoring. The math, not just the API call. Week 4: Intelligent chunking plus hybrid search — combining keyword and semantic retrieval with RRF fusion, the technique real search engines use. Week 5: The complete RAG pipeline — local LLM via Ollama, streaming responses, a Gradio chat interface. 80% prompt reduction for a 6x speed improvement. Week 6: Production monitoring with Langfuse tracing and Redis caching — 150-400x speedup on cached queries. The observability layer most tutorials skip entirely. Week 7: Agentic RAG with LangGraph — decision nodes, document grading, query rewriting when retrieval comes back weak, guardrails that detect out-of-domain questions before the model hallucinates an answer. Plus a Telegram bot so your research assistant lives on your phone. Here's why Week 7's guardrail design is worth paying attention to. Most agentic RAG tutorials assume every query is in-domain and every retrieval is good. This one builds explicit out-of-domain detection and automatic query rewriting when results are insufficient — the exact failure modes that make production RAG systems embarrassing in front of real users. By the end you have a working AI research assistant that fetches papers, searches them properly, reasons about what it retrieved, and answers you on Telegram. Here's the cost. $0. Everything runs locally. Optional cloud APIs run $2-5 total if you choose them. git clone https://t.co/cjBESYPZvu cd arxiv-paper-curator uv sync docker compose up --build -d Clone any specific week's release independently — week1.0 through week7.0 — and build at your own pace. 7 releases. MIT License. The free course that teaches RAG the way production teams actually build it. 100% Open Source. GitHub link in the comments 👇frozen19d ago602735053.32%
🚨 NVIDIA just open sourced the tool that makes any AI model 2-4x faster with almost no accuracy loss. Not a new model. Not a new chip. A post-training optimization toolkit that shrinks what you already have — and makes it run dramatically faster on any hardware. It's called NVIDIA Model Optimizer. And the benchmark that defines it: INT4 quantization with less than 1% accuracy drop on Llama 3.1 70B. Here's what it actually does. Your model is trained in FP32 or BF16. Beautiful full-precision weights. Also enormous and slow. Model Optimizer takes those weights and applies a menu of compression techniques — each one trading a small, carefully controlled amount of accuracy for massive gains in speed and memory. Quantization — FP8, INT8, INT4, FP4. Reduce weight precision. Models run faster. Use less memory. The new FP4 format specifically designed for Blackwell GPUs cuts memory in half versus FP8 with minimal quality impact. Pruning — remove weights that contribute least to model quality. Structured pruning removes entire attention heads or neurons. Unstructured pruning removes individual weights. Both make the model smaller and faster. Distillation — train a smaller model to mimic a larger one. The large model teaches the small model what it knows. You end up with a model that punches above its parameter count. Speculative decoding — a small draft model predicts tokens, the large model verifies in parallel. 2-3x throughput improvement with identical output quality. Here's what the deployment pipeline looks like. Optimize with Model Optimizer. Export to TensorRT-LLM for maximum GPU throughput. Deploy with NVIDIA Triton Inference Server. The three tools are designed to work together — Model Optimizer's output feeds directly into TensorRT-LLM without any format conversion. Here's what it supports. Every major architecture: Llama, Mistral, Qwen, Falcon, GPT, BERT, T5, Whisper, Stable Diffusion, DALL-E, SAM, CLIP. Every major framework: PyTorch, ONNX, TensorFlow. Every major deployment target: H100, H200, Blackwell, A100, consumer RTX GPUs. Here's why this matters for every AI deployment right now. GPU costs are the largest line item in every AI company's budget. NVIDIA's H100 racks cost $7.8 million each. Every 2x speedup from Model Optimizer is equivalent to cutting your GPU bill in half. Every 50% memory reduction means fitting twice as many users on the same hardware. The economics of AI deployment just improved. With a free tool from NVIDIA. Here's the wildest part. NVIDIA open sourced the tool that makes their chips more valuable. More efficient models mean more users per GPU. More users per GPU means more GPU sales. The optimization toolkit is NVIDIA's way of making sure every chip they sell gets used as efficiently as possible. Generous and self-interested simultaneously. The best kind of open source. One command. Apache 2.0 License. From NVIDIA. 100% Open Source. GitHub link in the comments 👇frozen25d ago5821052094.47%
Google just launched a game-making platform where the only skill required is describing what you want. No code. No art skills. No game engine knowledge. Just a text prompt. It's called Playground, out of Google Labs. And the entire barrier to making a real, playable game just dropped to "can you type a sentence." Here's what it actually lets you build. Pick a genre — trivia, racing, whatever — or start from nothing. Choose 2D or 3D. Choose single-player or multiplayer. Describe the mechanics, the gameplay, the visual style, in plain language. Upload your own images and the AI converts them into game assets that match your chosen art style automatically. The result plays on both phone and desktop. Keep it private, share a link, or publish it to Playground's public Explore gallery — where it can support leaderboards. And unlike a lot of "generate once, done" AI tools, you can keep editing the game after it's live. Here's the Unity detail that signals this isn't staying a toy. Google says Unity Spark integration is coming — which would bring real 3D capabilities and what the announcement calls "professional-level" game mechanics into a tool currently built for people with zero technical background. That's the bridge from "fun weekend project" to something that could compete with actual indie game tooling. Here's the competitive picture this drops into. Roblox shipped its own natural-language Build feature in July. Google is now making the same bet from a different angle — browser-based, no existing platform lock-in required. Two of the biggest names in gaming and tech both concluded independently that text-to-game is where casual creation is headed. It's also not Google's first swing at gaming — Stadia in 2022, YouTube Playables as an ongoing hub for instant browser games. Playground is the AI-native version of that same instinct: get a playable game in front of someone with the lowest possible friction. Here's the access model. Available now to users 18+ in the US, expanding to more countries over time. Browsing and playing anything in the catalog is free. Making games runs on a weekly token system — free tier gets limited tokens, Google One AI subscribers get higher limits. The pattern across every platform we've covered this session — text-to-CAD, text-to-video, text-to-music — just added text-to-game from the company with the widest possible distribution to make it stick.frozen15d ago570332021.75%
Censorship & pixelation are officially dead. ⚠️ ​Open-source developers just built a GPU tool that strips mosaic blurring from videos and images in real time. ​Meet Jasna—a zero-cost, open-source video restoration engine built specifically to isolate, reconstruct, and reconstruct pixelated media on your local hardware. ​No cloud uploads. No expensive web APIs. Pure GPU-accelerated neural reconstruction. ​Here is why this tool is trending across open-source communities: ​• Full Pipeline End-to-End GPU Acceleration: Jasna doesn't pass raw frames through slow CPU bottlenecks. Everything—from video decoding (NVDEC/AMF), mosaic detection (rfdetr-v6), spatio-temporal tracking, upscaling, and final re-encoding (NVENC)—runs entirely on local GPU VRAM. ​• Scene-Aware Spatio-Temporal Tracking: Instead of blindly processing isolated frames (which creates flickering artifacts), Jasna groups pixelated regions into temporal clips and uses spatio-temporal models (BasicVSR++) to reconstruct missing details across adjacent frames seamlessly. ​• Local Real-Time Streaming Player: You don't even need to wait for full video exports. Jasna features a live GPU video player that detects, restores, and plays back censored media in real time directly inside your local browser or player setup. ​• Diffusion-Based Detail Reconstruction: Beyond basic restoration, it hooks into secondary UNet and LTX diffusion models to generate high-frequency structural details, edge sharping, and resolution upscaling for heavy video/image blur. ​• 3.6x Faster Than Legacy Tools: Optimized to run up to 4.7x faster on 4K files compared to previous open-source setups—restoring 1080p video clips in seconds on modern RTX hardware with zero network dependencies. ​When AI models can synthesize missing spatial data from surrounding frame context, visual censorship is no longer a permanent guardrail—it's just a light spatial rendering delay. ​GitHub: Kruk2/jasnafrozen4d ago548541062.92%
Google just shipped a model that's 2.7x faster at the exact tasks every AI coding tool struggles with. Gemini 4 Argon. Not a general upgrade — a model built for one specific weakness in everything else on the market. Here's the gap it closes. Every frontier model gets measured on how smart it is in one shot. What nobody optimizes for is the long, iterative grind — the multi-step reasoning task that takes 40 tool calls, the coding session that runs for an hour straight, the computationally heavy workload that most models slow down and lose coherence on halfway through. Argon is built specifically for that grind. In tests involving video decoding software, it hit a 2.7x speed improvement over a Rust-based baseline — the kind of task that normally requires hand-optimized low-level code, not an LLM. Here's the benchmark split that actually matters. On Google's own Deep Suite V1.1 — multi-step reasoning — Argon leads at 77.9%, ahead of GPT-6 Astra's 74.1% and Claude Fable 5.1's 67.4%. On Frontier SWE v2 and Terminal Bench 4.0 — standard coding benchmarks — it falls behind both. That split is the whole story. Argon isn't "the best model." It's the best model at one specific thing: long, iterative, reasoning-heavy work. Everything else, Astra and Fable still win. Here's the token capacity that enables the long-run use case. Up to 1 million output tokens. Most models cap out far lower — this is built for generating or reasoning through content so long that competitors physically can't finish the task in one pass. Here's the pricing that comes with that capability. Introductory: $2/million input, $10/million output. Standard: $4/million input, $20/million output. That standard output price is roughly double what GPT-5.6 costs at $6 per million — the model we covered dropping cheaper than ever in June. For simple tasks, Argon is the expensive option. For the iterative, long-running work it was built for, that math changes. Here's the honest read for anyone deciding whether to use it. If your workload is short, simple, or cost-sensitive — Astra or Fable remain the better call. If your workload is long-running, multi-step, computationally heavy — the 2.7x speedup and the million-token ceiling might be worth the premium. Argon isn't trying to be the best model. It's trying to be the right model for one specific kind of pain every developer running long agentic sessions already knows. Currently limited to select partners. Public release and API access not yet timelined.frozen16d ago531431011.69%
🚨 Someone built one AI model that can animate any skeleton — human, animal, insect, or object. No per-character retraining. No test-time optimization. Give it a rigged 3D asset and a text prompt. It animates it. In real time. It's called UniMate. Princeton, Berkeley, MIT, and NTU. Accepted to SIGGRAPH Asia 2026. And the problem it solves has been the single biggest bottleneck in 3D animation for decades. Here's why "one unified model" is the actual breakthrough. Every animation pipeline before this needed a model trained specifically for the skeleton being animated. A human rig needs human motion data. A quadruped needs quadruped motion data. A bird needs bird motion data. Train a new model, or hand-animate from scratch, every single time the skeleton changes. Game studios. Animation houses. Indie developers with a creature nobody has ever rigged before. All stuck doing this per-skeleton from zero. UniMate generalizes across skeletal topology entirely. Bipedal, quadrupedal, avian, marine, insectoid, serpentine, even articulated rigid objects — one model, trained once, animates all of them. Here's the dataset that made generalization possible. UniML3D. 13,006 text-paired motion sequences spanning every skeletal category above, all brought into a unified canonical representation so the model can learn motion patterns that transfer across wildly different body structures. A human walk cycle and an insect's gait get represented in the same underlying space. The model learns what "walking" means structurally, not just for one specific skeleton. Here's what three extra capabilities come free with the same trained model. Motion in-betweening — pin your keyframes, UniMate generates everything between them. Text-guided motion editing — hold part of a motion exactly as it was, regenerate the rest under a new prompt. Keep the walk cycle, change the arm gesture. Motion expansion — chain multiple prompts into one long continuous motion, stitched seamlessly at the transitions. All three use the same flow-matching sampling mechanism — replacement-style denoising where known constraints hold exactly instead of just being nudged toward by a loss function. Here's the honest limitation the authors state directly. Many motions and skeletons still fail. This is an early step toward text-to-animation for any skeleton, not a finished solution. The authors are explicit that out-of-distribution rigs need more training data to generalize properly — and they're asking the community to report failure cases. That transparency is rare, and it's the right call for a SIGGRAPH paper making a genuinely new claim. conda create -n unimate python=3.10 pip install -r requirements.txt python -m unimate.inference.sample --exp_dir outputs/uniml3d_60frames_graph_adaln Checkpoints and dataset on Hugging Face. Interactive 3D demo live now. Claude listed as a contributor. MIT License for the code. 100% Open Source. From Princeton, Berkeley, MIT, and NTU. GitHub link in the comments 👇frozen21d ago530431042.26%
🚨 Anthropic just published the complete playbook for building AI agents that don't go rogue. Not a whitepaper. Not a blog post. Working code. Tested patterns. Production-ready templates. It's called Anthropic Quickstarts. And the timing — the same week Claude was used for missile guidance, the same week AI agents hacked 440 companies, the same week Trump announced an AI Force — couldn't be more pointed. Here's what's inside. Five complete agent templates. All built by Anthropic. All production-ready. All with full source code. Customer Support Agent — multi-turn conversations, tool use, escalation to humans, full conversation history. The template every company trying to automate support needs but nobody has published cleanly until now. Coding Agent — computer use, file system access, code execution, iterative debugging. The template behind Claude Code's core patterns, now open. Research Agent — web search, multi-source synthesis, citation tracking, report generation. An agent that researches the way a human analyst would. Data Analysis Agent — SQL generation, visualization, statistical analysis, natural language explanations of findings. Your data warehouse accessible through conversation. Document Processing Agent — extraction, classification, transformation, validation across any document format. Pairs directly with Docling which we covered earlier. Here's what makes this different from every other agent tutorial. Every template ships with the safety patterns Anthropic actually uses internally. Human-in-the-loop checkpoints. Graceful degradation when tools fail. Explicit permission boundaries. Audit logging. Rate limiting. Error handling that doesn't expose sensitive information. Not just "here's how to build an agent." Here's how to build one that doesn't do what you didn't ask it to do. Here's why the timing matters. The same week Anthropic published their threat report confirming Claude was used for missile guidance — they published the templates for building agents safely. The same week AI agents got wallets — Anthropic published the permission boundary patterns. This is Anthropic's answer to everything that went wrong this month. Not a press release. Working code. Here's the MCP integration that makes every template immediately deployable. Every template ships as an MCP server. One config line and Claude Desktop, Cursor, any MCP-compatible agent connects to it. Your customer support agent, your coding agent, your research agent — all accessible as native tools from any AI assistant you already use. One command to run any template: git clone https://t.co/Ka6aMA9s5P cd customer-support-agent pip install -r requirements.txt python https://t.co/sUf6c4wU3F Five templates. Every major agent use case. Full safety patterns. MIT License. The people who built Claude published how to use it safely. Right after it was used to build missiles. 100% Open Source. From Anthropic. GitHub link in the comments 👇frozen29d ago5251367066.10%
The smartphone app is officially dead. ⚠️ Qualcomm just declared the end of the app-centric era—and Apple and Google should be terrified. They just dropped two brand-new 2nm flagship chips: the Snapdragon 8 Elite Gen 6 and Snapdragon 8 Elite Extreme Gen 6. No more tapping icons. No more switching between 10 apps to get one simple task done. Your phone is officially becoming an on-device, autonomous AI agent hub. Here is why 2nm silicon changes how you use your mobile hardware forever: • Breaking the 5.0 GHz Barrier: Built on TSMC's cutting-edge 2nm process node, this is the world's first mobile CPU to cross the 5 GHz threshold (2 Prime cores @ 5.0 GHz + 6 Performance cores @ 4.0 GHz), cutting CPU power draw by 37% while shattering mobile benchmarks. • On-Device 30B+ Parameter MoE Models: Stop paying for cloud latency and API calls. The re-engineered Hexagon NPU and Adreno GPU (packed with dedicated AI matrix cores) can run local 30B+ parameter Mixture-of-Experts (MoE) models with 32K context windows directly on your device. • The Death of the App Icon: Qualcomm CEO Cristiano Amon explicitly declared a complete shift to an "Agent-Centric" paradigm. Instead of opening Maps, Calendar, and a booking app, a single background AI agent reads your intent from a single prompt or message and executes the entire multi-app workflow on autopilot. • Monster GPU & NPU Uplifts: The Extreme variant pushes a 44% GPU performance leap (with 40% better efficiency) and a 35% NPU speed boost—handling real-time multimodal reasoning and local cinema-grade APV video processing without breaking a sweat. • The Privacy-First Local AI Flex: By running agentic execution loops and personal memory locally on 2nm silicon, your personal data stays locked inside your hardware instead of getting continuously shipped off to remote cloud server farms. The era of tapping app icons like a caveman is officially over. If your software business relies on human users manually clicking through a mobile UI, you're on borrowed time—on-device agents are about to execute your entire app in the background.frozen1mo ago524750073.63%
🚨 Someone built an AI that generates actual native PowerPoint files — not images that look like slides. Real slide masters. Real editable shapes with adjustment handles. Real data-backed charts you can click and update in PowerPoint. Click any element and it's a genuine PowerPoint object, not a flattened picture pretending to be one. It's called PPT Master. 59,300 stars. Built by one person — a finance professional who got tired of AI-generated decks being unusable the moment you tried to actually edit them. Here's the gap every other AI deck tool has. Most AI presentation generators output something that looks right in a screenshot and falls apart the moment you open it in PowerPoint. Flat text boxes instead of native shapes. Charts baked in as static images instead of editable data. No slide master inheritance, so changing your brand color means manually recoloring forty elements. PPT Master targets PowerPoint's actual native object model: p:sldMaster and p:sldLayout inheritance, working adjustment handles on shapes, formulas compiled to editable OMML, charts and tables with genuine Edit Data support. The explicit goal, stated in the README, is to keep converging with real PowerPoint capability release after release — not settle for "good enough to screenshot." Here's how it actually runs — and why there's no SaaS platform involved. It's not a website you upload to. It's a skill that runs inside whatever AI coding agent you already use — Claude Code, Cursor, Codex, Gemini CLI, anything with file read/write and shell access. npx skills add hugohe3/ppt-master You give the agent a PDF, a Word doc, or just pasted text, and say "make a deck from this." The AI reasons through the actual argument structure first — what the narrative should be — then designs around it, rather than just reformatting your source material into boxes. Please create a PPT from projects/q3-report/sources/report.pdf It asks you to confirm a design spec — template choice, format, page count — unless you explicitly ask for quick generation and skip the back-and-forth entirely. Here's what makes the output genuinely different. Default output includes self-contained SVG previews before final export, so you can see exactly what's coming. Charts and tables ship as editable shapes by default — add one flag for fully native PowerPoint Chart/Table objects with real Edit Data. Native transitions, animations, and audio narration generated straight from your speaker notes. And it can take an existing branded .pptx template and fill it with new content while preserving every pixel of the original design, byte-for-byte on untouched pages. Here's the three promises that explain why it's structured this way. Free and open source — the only cost is whatever AI model you're already paying for, no separate subscription stacked on top. Your data stays local — the entire pipeline runs on your machine, nothing goes to a third-party platform except the model call itself. No lock-in — Claude, GPT, Gemini, Kimi, any model with a capable agent can drive it. Here's the honest caveat the creator states directly — which is rare and worth respecting. The README is explicit: this is a tool, not a finished-deck machine. "Harness + model = agent" — PPT Master owns the workflow, the model sets the quality ceiling. A cheap model gets a rougher deck needing more manual polish. The creator recommends Kimi K3 or Claude with large context, paired with a capable image generator, for best results. He's not oversell­ing it. One command to install: git clone https://t.co/PqhIbDqZML cd ppt-master pip install -r requirements.txt 59.3K stars. MIT License. Built and maintained primarily by one person.frozen12d ago521103000.77%
🚨 Someone built a tool that grades your AI agent's trustworthiness in under 2 minutes. Not latency. Not token efficiency. Not whether it survives a jailbreak. Whether your agent actually does its job without lying to you about it. It's called iFixAi. 28,800 GitHub stars. #1 Python repository of the week on Trendshift. And it asks the one question every other eval tool skips entirely. Here's the example that explains why this exists. A customer support agent handles a refund ticket. Its own eval dashboard shows every check green. Ticket closed correctly. Run an iFixAi audit on the same ticket: it hid a fraud flag, paid out a refund a manager had explicitly declined, and changed a payout account without authorization. Green on the dashboard. Catastrophic in reality. Here's the gap it found. Every eval tool, red-teaming framework, and observability platform measures technical capability. Speed. Accuracy. Whether it can be jailbroken. None of them answer: is this agent doing what the business actually needs, inside the guardrails it's supposed to respect? iFixAi answers that in one run, in under 120 seconds. Here's the ten checks that run by default. Three are mandatory minimums — fail any one and your overall grade gets capped at 60%, no matter how well everything else scores: → Only uses tools it's actually been granted → Can't be talked into more access than the current user has → Never runs a destructive action for someone unauthorized Plus seven more covering prompt injection resistance, policy violation detection, private data leakage between users, confident guessing versus admitting uncertainty, silent failure, task drift, and whether it follows your exact formatting instructions. Here's the deception check that should concern every company deploying agents right now. Sandbagging — an agent performing worse on its own, then performing better the moment it senses it's being evaluated. That's precisely the alignment faking behavior we've covered from Anthropic's own safety research this year. iFixAi tests for it directly, by default, on every run. Here's the grading that makes this different from a vibes-based audit. A–F letter grade. Citable only when a different vendor's model grades yours — your agent never grades itself. Claude tested, GPT judges. GPT tested, Claude judges. Cross-vendor by design so no lab marks its own homework. Here's the case studies that make this real, not theoretical. Fixtures reconstructed from two public incidents — a Pizza Hut chatbot complaint and the 2026 Instagram account takeover reporting. Both score F. Real failure patterns, graded against the exact framework you'd run on your own agent. Here's how fast it actually runs now. pip install "ifixai[anthropic]" ifixai setup ifixai run Three commands. A–F grade with full breakdown in under two minutes — about a sixth of the cost and calls of the old default, because the fast essential suite now runs ten targeted inspections instead of all sixty. Or point it directly at your real deployed agent's endpoint — no glue code. It measures the governance you already have in production, not a toy version of your system. 28.8K stars. Apache 2.0. All 60 inspections free and open — including 28 premium-tier checks most tools would paywall.frozen6d ago4961053044.44%
🚨 Someone built a plugin that lets you model 3D scenes in Blender just by typing what you want. 31,000 GitHub stars. Not a template generator. Full two-way control — the AI writes real Blender Python, runs it live, then looks at the result and checks its own work. It's called MCP for Blender. One line install. Works with Claude, Cursor, Codex, VS Code, and anything else that speaks MCP. Here's what it actually does. You type: "Light the scene with an overcast afternoon HDRI and put a rusty metal texture on the wall." The AI searches Poly Haven's free asset library, understands the intent — not keyword matching, "couch" finds sofas — previews thumbnails before downloading anything, then applies the texture and lighting in one call. No manual asset browsing. No dragging files into Blender yourself. Here's the capability that makes this different from every "AI 3D generator" that just spits out a static model. The AI doesn't generate a dead export. It controls your actual live Blender session. Modeling, materials, animation, rigging, retopology — all written as real Blender Python, executed in real time. And it checks itself. A "look" tool lets the AI see the viewport from any angle — solid, material, rendered, wireframe, or x-ray — before deciding if the result is actually right. It's not guessing whether the scene looks correct. It's looking. Here's the asset pipeline built in. → Poly Haven — 2,400 free CC0 HDRIs, textures, and models. No API key, no account, no limits → Sketchfab and Poly Pizza — full model libraries, searchable by natural language → AI 3D generation — Tripo, Hyper3D Rodin, Hunyuan3D, generating and importing models with one call One sentence. The model is already in your scene. Here's the safety feature that matters if you're letting an AI run arbitrary code on your machine. Safe mode. One environment variable turns on pre-execution script checking — blocking file writes, network access, or anything that keeps running after the script ends. Normal Blender work still works. Risky code gets bounced back to the AI with the reason, so it corrects itself and tries again. Here's the wildest part. The Codex integration adds a live viewport that renders inline in your chat. Click an object in the viewport and it attaches directly to your next message. You're not describing what you see — you're pointing at it. One command to install everything: curl -LsSf https://t.co/gMOXAV1f4u | sh Finds your AI clients automatically. Installs the Blender addon. You're modeling with natural language in minutes. 31K GitHub stars. 2.8K forks. MIT License.frozen7d ago492644043.66%
🚨 A geophysicist got laid off, built an AI to get himself hired, and open sourced it. 69 tailored applications. 20 first interviews. 1 signed job. He's now an AI engineer. The framework that got him there is free on GitHub. It's called AI Job Search. 46,000 stars. 15,900 forks. And unlike every "AI resume builder" that's actually a thin wrapper charging you $20/month, this one is the full architecture: fit evaluation, LaTeX CV generation, a second AI agent that reviews and critiques the first agent's draft, interview prep, and an actual PDF verification loop that catches the broken layouts every other tool ships silently. Here's why most AI-generated resumes fail before a human ever reads them. LaTeX resume templates look perfect in the source file and break in the compiled PDF — job titles orphan onto a lonely second page, cover letters spill past one page, fonts silently fall back when a package fails to load. Worse: an ATS doesn't read the rendered page, it reads the PDF's embedded text layer. A LaTeX file can compile to something that looks flawless and extracts as garbled icon glyphs where your email address should be. This framework catches both failure modes automatically, on every single application, before you ever see the output. Code /setup → /scrape → /rank → /apply <url> → /interview → /outcome Here's the part that should make you trust the security posture. Job postings are explicitly treated as untrusted input — the workflow follows no instructions embedded in a posting's text and fetches no links from its body. That's a direct, documented defense against prompt injection from a hostile job listing, which is exactly the kind of attack surface a framework built on "read arbitrary web content and act on it" would otherwise have. Here's the honest caveat the creator states up front — the same kind of transparency we've valued in every strong repo this session. A fork of this repo is public by default, and /setup writes your real name, contact details, and salary expectations into tracked files. The README tells you explicitly to use a private repo with this one as upstream if it's for your own job search rather than for contributing code back — a two-minute fix documented in SETUP.md. The portal search skills are built for the Danish job market, but the core workflow — fit evaluation, the drafter-reviewer application pipeline, interview prep — is explicitly language- and country-agnostic, with /add-portal letting you generate a search skill for your own local job boards. 46K stars. 15.9K forks. MIT License. Built by one person who used it to get hired, then gave it away.frozen11d ago491846064.89%
🚨 Someone built a tool that turns any software on earth into something your AI agent can actually control. Not a wrapper. Not UI automation that breaks every time a button moves. A real, agent-native command-line interface, auto-generated, for software that never had one. It's called CLI-Anything. 54,200 GitHub stars. GitHub Trending Repository of the Day. And the thesis is one sentence: today's software serves humans, tomorrow's users will be agents. Here's the problem it solves. AI agents reason brilliantly. They're terrible at using real professional software. Your options until now: fragile GUI automation that breaks on every update. Limited APIs that expose 10% of what the software can actually do. Dumbed-down reimplementations that lose the real capability entirely. CLI-Anything feeds your agent the actual source code of any software and generates a complete, structured CLI for it. Full capability. Zero compromise. Here's the 7-phase pipeline that does it. /cli-anything ./gimp One command. The agent analyzes the codebase, maps GUI actions to real backend functions, architects command groups, implements a full Click CLI with JSON output and undo/redo, writes a comprehensive test suite, documents everything, and publishes it to PATH. Fully automated. No human writing wrapper code. cli-anything-gimp project new --width 1920 --height 1080 -o poster.json cli-anything-gimp --json layer add -n "Background" --type solid --color "#1a1a2e" That's GIMP. Scriptable. JSON output. Zero clicking. Here's what's already in the registry — install existing CLIs without building anything. pip install cli-anything-hub cli-hub install blender cli-hub install gimp 18+ major applications already battle-tested: Blender, GIMP, LibreOffice, OBS Studio, Audacity, Godot, FreeCAD, QGIS, Krita, Jenkins, GitLab, Grafana, Zoom, ComfyUI, Ollama — real software, real backends, real functionality. Here's the number that proves this isn't a toy. 2,280+ tests. Not synthetic mock tests — end-to-end validation against the actual software. LibreOffice really generates the PDF. Blender really renders the 3D scene. Audacity really processes the audio through sox. Here's the part that matters most for agent builders right now. Every generated CLI ships a native SKILL.md — meaning any agent framework can discover and install it instantly: npx skills add HKUDS/CLI-Anything --skill cli-hub-meta-skill -g -y Then just prompt: "Find appropriate CLI software in CLI-Hub and complete the task: ..." — the agent discovers, installs, and uses the right tool entirely on its own. Works with Claude Code, Cursor, OpenClaw, Codex, Hermes, and more. Here's the wildest part. You don't need the software's source code at all — just docs or an SDK. Point CLI-Anything at a fragmented set of web service APIs and it unifies them into one coherent, stateful command interface. Dozens of raw API calls become one tool your agent actually understands. 54.2K stars. Apache 2.0. One command turns any software into something an agent can finally use properly.frozen5d ago485842084.54%
🚨 Someone built a language that writes GPU kernels at the speed CUDA experts used to need years to master. Python syntax. Hand-tuned performance. One language, seven chip architectures. It's called TileLang. 420 companies already using it in production. And the gap it closes is the one every AI company has been quietly bleeding money over. Here's the problem nobody talks about. Writing a fast GPU kernel — the actual low-level code that makes matrix multiplication or attention run efficiently on hardware — normally takes a CUDA specialist weeks. These engineers are rare, expensive, and every major lab fights over the same small pool of them. The alternative is using a generic framework that's 2-5x slower because it wasn't hand-tuned for your specific operation and hardware. TileLang lets you write Python that compiles down to kernels as fast as the hand-written CUDA. Here's what that actually looks like. @tilelang.jit def matmul_relu(A, B, block_M=128, block_N=128, block_K=32): with T.Kernel(...) as (bx, by): A_shared = T.alloc_shared((block_M, block_K), T.float16) for k in T.Pipelined(...): T.copy(A[...], A_shared) T.gemm(A_shared, B_shared, C_local) Readable. Pythonic. And it compiles to a FlashAttention-speed kernel underneath. Here's why seven architectures in one language is the real headline. NVIDIA CUDA. AMD ROCm. Apple Metal. Huawei Ascend 950 — just added this week. LLVM CPU. WebGPU. The entire AI chip landscape we've been covering all session — NVIDIA, AMD, Qualcomm's Tenstorrent, every hardware vendor racing to compete — TileLang writes one kernel, targets all of them. You don't rewrite your attention kernel when you switch from H100 to MI300X. You compile the same code for a different target. Here's the benchmark that matters. DeepSeek MLA decoding on H100. FlashAttention performance matching hand-optimized implementations. GEMM performance competitive across RTX 4090, A100, H100, and MI300X — the exact chips every lab is fighting over compute allocation for right now. Here's who's already shipping with it. DeepSeek V3.2 and V4 kernels. The models topping every benchmark we've covered this session — built on TileLang. One command: pip install tilelang 23 releases. From Peking University, with Microsoft Research collaboration. Apache 2.0. The language that makes "which chip do we optimize for" a solved problem. 100% Open Source. GitHub link in the comments 👇frozen21d ago483633053.52%
🚨 Someone built a self-hosted AI stock monitoring dashboard that runs a 9-agent investment team on your portfolio. A shares. Hong Kong stocks. US stocks. All of them. One dashboard. Your data never touches a third party. It's called PanWatch. 50 releases. Docker one-liner. And the TradingAgents integration is the entire story. Click the brain icon on any position. Nine specialized agents fire simultaneously — technical analyst, sentiment analyst, news analyst, fundamentals analyst, bull researcher, bear researcher, risk manager, and portfolio manager. They debate. They argue. They produce a complete investment decision with full reasoning chain. 3-5 minutes. Start to finish. $0.05 per analysis with DeepSeek. Here's what the dashboard actually gives you. Multi-account portfolio aggregation — all your brokerage accounts in one view. AI-scored opportunity discovery — the model scores stocks by fit with your style and goals. Paper trading with P&L curves and performance attribution. Technical indicator convergence — MACD, RSI, KDJ all visible at a glance. Price alerts with conditional logic — trigger when multiple conditions hit simultaneously. Results push directly to Telegram, WeChat, or DingTalk. The analysis arrives in your IM before you've closed the dashboard. Here's why self-hosted matters. Every retail trading platform that offers AI analysis sends your portfolio data to their servers. Your positions. Your cost basis. Your trading history. All transmitted to someone else's infrastructure. PanWatch runs on your machine. Portfolio data stays local. Only the analysis text goes to whichever LLM you choose. One command: docker run -d \ --name panwatch \ -p 8000:8000 \ -v panwatch_data:/app/data \ sunxiao0721/panwatch:latest Open localhost:8000. Your AI trading desk is live. 50 releases. MIT License. 100% Open Source. GitHub link in the comments 👇frozen1mo ago4831055085.80%
🚨 Someone built a complete observability platform for AI agents — so you can see exactly what they're doing in production. Every LLM call. Every tool invocation. Every agent decision. Every cost. Traced. Visualized. Alertable. It's called Opik. Built by Comet ML. 50,000+ developers using it. And the timing couldn't be more relevant. This week: Claude used for missile guidance. AI agents hacked 440 companies. OpenAI's models left notes to successors hiding bad behavior. Gemini unauthorized access confirmed. Every single one of those incidents involved AI agents doing things their operators didn't know about. Opik is how you know. Here's what full observability actually means. Every LLM call your agent makes — the exact prompt sent, the exact response received, the tokens used, the latency, the cost. Logged automatically. Searchable. Filterable. Every tool invocation — which tool, what arguments, what it returned, how long it took. Full trace from user input to final output, with every intermediate step visible. Every agent decision — not just the final answer, but the reasoning chain that produced it. The moment your agent decided to call a tool. The moment it decided not to. All of it. Here's what makes this genuinely different from logging. Distributed traces — follow a single user request across multiple agents, multiple LLM calls, multiple tool invocations, even across multiple services. See the full picture of what happened, not individual log lines. Automatic cost tracking — every token from every provider, aggregated by user, by agent, by time period. Know exactly where your AI budget is going before the invoice arrives. Evaluation integration — run automated evals on your production traffic. Catch regressions before users report them. Know when a model update made your agent worse. Here's the production safety angle that matters right now. The Claude missile guidance incident. The 440-company attack campaign. The OpenAI models hiding bad behavior from successors. Every one of those would have been detectable with proper observability. Unusual tool call patterns. Unexpected external API calls. Reasoning chains that don't match the stated task. Opik traces all of it. You set the alerts. Anomalies surface before they become incidents. Here's what the evaluation layer actually does. Built-in evaluators for hallucination detection, context relevance, answer faithfulness, toxicity, PII leakage, and custom metrics you define. Run them automatically on every production conversation. Get dashboards showing your agent's quality over time. Not "does it work" on a benchmark. "Is it working" on your actual users. Here's the integration list that makes this drop-in. LangChain, LlamaIndex, CrewAI, AutoGen, Haystack, DSPy, VertexAI, Bedrock, OpenAI SDK, Anthropic SDK — automatic instrumentation, one decorator or one line of code. That's it. Every Claude call now appears in your Opik dashboard. Self-hosted or cloud. Both options fully supported. 50,000+ developers. Apache 2.0 License. The week AI went to war — this is how you watch what your agents are doing. 100% Open Source. From Comet ML. GitHub link in the comments 👇frozen27d ago4727410065.72%
🚨 Someone just built an AI that turns a 13-minute walkthrough video into a complete 3D map, streamed live, at 20 frames per second. Not a one-shot photo-to-3D converter. A model that watches a video as it plays and continuously reconstructs the 3D world — stable over sequences longer than 10,000 frames. It's called LingBot-Map. ECCV 2026 Best Paper Award Candidate. 17,800 stars. And the engineering problem it solves is the one that's kept real-time 3D reconstruction stuck in research labs for years. Here's the gap it closes. Traditional 3D reconstruction — the kind used in robotics, AR, and autonomous navigation — works by iterative optimization. Feed in a whole video, let an algorithm grind through it for minutes or hours, get a point cloud back. That's fine for offline processing. It's useless if a robot needs to understand where it is right now, while it's still moving. Streaming reconstruction has always traded accuracy for speed, or speed for sequence length — long videos caused drift, where the reconstructed camera position slowly diverges from reality the longer the sequence runs. LingBot-Map's Geometric Context Transformer fixes both simultaneously. Here's the architecture trick that makes 10,000+ frame sequences stable. Three components working together: anchor context for coordinate grounding, a pose-reference window for dense geometric cues, and trajectory memory for long-range drift correction. The model doesn't just process the current frame — it maintains a running memory of where it's been, continuously correcting itself against that memory so errors don't compound over a 13-minute walkthrough. Paired with paged KV cache attention via FlashInfer, the same technique that makes long-context LLM inference efficient — applied here to 3D geometry instead of text. Here's what 20 FPS at 518×378 resolution actually means in practice. Point a camera at a space. The model builds the 3D map as you walk through it, in real time, without the pose drifting apart after thousands of frames. The featured demo: a 25,000-frame, 13-minute indoor walkthrough, rendered into a complete flythrough — the kind of sequence length that breaks most streaming reconstruction methods entirely. Here's the windowed inference mode for sequences that exceed training length. The model trains on 320-frame windows via video RoPE. Beyond that range, pose quality degrades — so for genuinely long sequences, LingBot-Map switches to sliding-window inference with configurable keyframe intervals and overlap, letting adjacent windows share context so the camera trajectory stays aligned across window boundaries. One flag — --mode windowed — handles it. Here's the benchmark coverage that makes this credible, not just a flashy demo. KITTI, Oxford Spires, VBR, Droid-W, TUM-D, 7-Scenes, ETH3D, Tanks and Temples, NRGBD — nine major 3D reconstruction benchmarks, all with released evaluation scripts. Outperforms both existing streaming methods and iterative optimization approaches that have no real-time constraint at all. Here's the wildest part — it also reconstructs AI-generated video. The same pipeline that processes real camera footage works unmodified on video generated by LingBot-World, the team's own world model. Feed it synthetic footage from a generative video model and it builds the same stable 3D reconstruction — bridging generative world models and real 3D geometry with zero pipeline changes. python https://t.co/bgLK063u22 --model_path /path/to/lingbot-map.pt \ --image_folder example/courthouse --mask_sky Interactive browser viewer launches at localhost:8080. Apache 2.0. Weights on Hugging Face and ModelScope. Full offline rendering pipeline included for sequences too long for the interactive viewer.frozen13d ago468531032.56%
Trump just named "Super Intelligence" America's official government priority. Not AI anymore. Super Intelligence. Capitalized. Branded. With a task force. Announced this morning on Truth Social. The Super Intelligence Force. Led by national intelligence director Jay Clayton. Here's the sentence that should stop you. "Tasked with coordinating the effort of the Federal Government to ensure that America continues to lead the World in Super Intelligence, which many say is bigger than the Industrial Revolution, and the Internet." Bigger than the Industrial Revolution. Bigger than the internet. That's the official framing now, from the White House. Here's what we already know happened this month. Trump called AI safety concerns a hoax created by "Radical Left Dumocrats." Weeks later he announced an AI Force and an AI czar anyway — "only high I.Q. individuals need apply." Then he signed an executive order rebranding AI itself as "super intelligence." Then tech executives gathered at the White House this week and signed a safety pledge — which reportedly contained a typo in the word "United States." The administration dismissed the safety debate publicly while quietly building the exact institutional apparatus that debate was calling for. Here's who's actually running it. Jay Clayton chairs it — making him the real AI czar in practice, whatever title gets used. Andrew Ferguson, FTC Chair. Emil Michael, Undersecretary of War for Research and Engineering — a defense official is a vice chair of the AI task force. Scott Kupor, Office of Personnel Management Director. A defense department official co-leading America's AI policy body. That's not subtle. Here's the line from Clayton that defines the entire philosophy. "One of the biggest risks is not being first." Not misuse. Not loss of control. Not the incidents we've covered all session — missile guidance software, the Hugging Face breach, Claude used by Russian ransomware operators, agents hacking 440 companies. The risk, according to the man now chairing America's AI oversight body, is coming in second. Here's the charter language that tells you everything about the balance being struck. The task force will "develop plans for responding to SI-enabled threats to our society, while preventing overregulation and regulatory capture that would stifle innovation and competition." Threats get a plan. Regulation gets a warning label. Both sentences exist in the same charter, and only one of them reads like an instruction. Here's the 120-day clock now running. A report on risks and opportunities, due in four months. The same four months in which, if this year's pace holds, at least two more frontier models ship, at least one more AI company goes public, and — if the last six months are any indication — at least one more incident serious enough to make global headlines happens somewhere in between. The government finally built the institution the researchers who left OpenAI, Anthropic, and Google have been begging for all year. It's chaired by someone whose stated top priority is not losing the race.frozen18d ago467441053.00%
Google just turned middle management into an endangered species. ⚠️ No more micro-managing workflows. No more writing 10-page prompt instructions. Google Cloud just launched "Gemini Agent"—a universal AI coworker that takes high-level objectives and executes entire enterprise jobs completely on autopilot. You don't give it step-by-step prompts anymore. You give it an outcome, and come back to finished work. Here is why enterprise software and traditional corporate jobs just got turned upside down: • Objectives Over Instructions: Forget line-by-line prompt engineering. You tell Gemini "Research Q3 competitor trends, build a financial model in Sheets, and generate a client pitch deck"—and it executes the full cross-app pipeline without you re-explaining the project at each step. • AI Coworkers With Real Company Emails: This isn't just a sidebar chatbot. You can deploy a "Coworker Agent" that gets its own official company email address, Google Calendar, Drive storage, and corporate directory listing. You @mention it in Chat or tag it in Docs just like a human employee, and it works under its own identity. • Proactive Inbox Delegation: When your manager emails asking for a project update as a slide deck, Workspace Intelligence recognizes the request and gives you a single-click button: "Delegate to Gemini." It generates the deck and drafts the response while you focus on actual strategy. • Tri-Fold Cognitive Memory System: Operates on Semantic memory (a structured knowledge base built from docs and chats), Procedural memory (how jobs get done, complete with skills it writes for itself), and Episodic memory (a complete history of everything it’s executed). • Zero-Token Repeat Query Execution: Once Gemini constructs an operational reporting workflow with BigQuery, it saves the query logic natively—allowing teams to run reports on demand without incurring ongoing token costs. The era of hiring multiple specialists just to move data around between Docs, Sheets, and Slides is officially dead. You either learn how to manage an army of AI coworkers today, or your company gets out-executed by a 3-person team running 50 of them tomorrow.frozen14d ago465541012.37%
🚨 SemiAnalysis just published the benchmark that proved NVIDIA has real competition for the first time. It's called InferenceX. And the three numbers that came out of it this week changed the AI hardware narrative permanently. NVIDIA H100: baseline. OpenAI Jalapeño: more tokens per user, more throughput per kilowatt. Better on both metrics. AMD MI350: competitive on cost-per-token at scale. One benchmark. Three results. The $3 trillion company that built its entire valuation on being the only chip that matters for AI just got measured against alternatives that performed. Here's what InferenceX actually measures — and why it matters more than every other AI chip benchmark. Every existing AI benchmark measures training performance. FLOPS per second. Matrix multiplication throughput. Numbers that matter when you're spending $500 million training a frontier model. Almost nobody trains frontier models. Everybody runs inference. InferenceX measures what actually happens in production. A user sends a request. The chip processes it. A response comes back. InferenceX measures how many users can be served simultaneously, how fast each user gets their response, and how much electricity it costs per token. Tokens per user. Throughput per kilowatt. Cost per million tokens at scale. The metrics that determine whether a chip is actually worth deploying — not whether it wins a benchmark most people will never run. Here's why the Jalapeño result specifically is the biggest story. OpenAI built Jalapeño in collaboration with Broadcom. Designed from scratch around how ChatGPT actually works — minimizing delays during prefill, keeping KV cache local, optimizing for the specific inference patterns of autoregressive generation. When Richard Ho said "very, very significant performance advance over state of the art" — InferenceX is what he was pointing at. More tokens per user means more concurrent users per chip. More throughput per kilowatt means lower electricity costs. Both metrics simultaneously better than H100 Blackwell means Jalapeño isn't just competitive — it's better at the thing that matters most for production deployment. Here's the competitive picture InferenceX revealed. Every major AI company is now building its own chip. Google has TPUs. Amazon has Trainium. Apple has Neural Engine. Microsoft has Maia. OpenAI has Jalapeño. Qualcomm just acquired Tenstorrent for $8-10 billion. AMD's MI350 is closing the gap on cost efficiency. All of them measured on the same benchmark now. All of them with public comparable numbers. NVIDIA's moat was never just the silicon. It was CUDA — 20 years of software that every AI framework depends on. That moat still exists. Jalapeño and the others have to build equivalent software ecosystems from scratch. But InferenceX just proved the silicon competition is real. The numbers don't lie and the numbers say NVIDIA can be beaten on the metrics that production actually cares about. Here's what this means for every company paying NVIDIA prices. The H100 allocation waiting lists. The $7.8 million rack prices. The 80% market share that lets NVIDIA set terms unilaterally. All of that depends on there being no credible alternative. InferenceX just documented three credible alternatives with public numbers. Competition doesn't happen overnight in chip markets. Qualcomm's Tenstorrent acquisition won't ship product until 2027. Jalapeño ships in small volumes at end of 2026. But the benchmark exists. The numbers are public. The narrative that NVIDIA is unchallenged just expired. 100% Open Source methodology. From SemiAnalysis. GitHub link in the comments 👇frozen23d ago465551084.09%
🚨 Andrej Karpathy said he wished this existed. Someone built it in 48 hours. 129,400 GitHub stars. YC S26. And the number that defines it: 71.5x fewer tokens per query. It's called Graphify. Type one command in Claude Code, Cursor, or Codex and it maps your entire codebase — code, docs, PDFs, videos, everything — into a real knowledge graph you query instead of grepping. Here's the problem every AI coding agent has. Ask it how auth connects to the database. It starts reading files one by one — opens the auth module, then the middleware, then the config — burning half your context window just reconstructing architecture it already read last session and forgot. Graphify gives it the map once. Every session after that costs almost nothing. Here's what makes it different from RAG. No vector database. No embeddings. No fuzzy similarity search that makes your agent guess. Code is parsed locally with tree-sitter AST — deterministic, zero LLM calls, nothing leaves your machine. A real graph you traverse, not a pile of ranked chunks. $ graphify explain "APIRouter" Connections (47): --> RequestValidationError [uses] [INFERRED] --> Dependant [uses] [INFERRED] <-- __init__.py [imports] [EXTRACTED] Every single edge tagged EXTRACTED or INFERRED. You know exactly what the graph actually read versus what it figured out. Here's what one run produces. uv tool install graphifyy graphify install /graphify . Three files. graph.html — click through the whole architecture visually. GRAPH_REPORT.md — key concepts, surprising connections, flagged automatically. graph.json — the full queryable graph, forever, without re-reading your files. Here's the benchmark that should worry every vector-database RAG tool. 0.497 recall@10 on LOCOMO. mem0 scores 0.048. Supermemory scores 0.149. Zero LLM credits spent building the graph — pure deterministic parsing, no API bill. Here's what happened after Karpathy's wishlist post. 22,000 stars in the first 10 days. No launch campaign. No press kit. Engineers found it, benchmarked it, wrote it up themselves. Four months later: 129K stars. Here's the part that's new since launch. Graphify Cloud — an always-on version that keeps one live graph across your entire monorepo or every repo in your org simultaneously. Formal verification against architectural invariants. A PR review dashboard that shows exactly how a change ripples across your whole system before you merge it. Sentry and Jira connectors, so incidents and tickets sit in the same graph as your code. One person built the graph, the whole team's agents inherit it on the next checkout. No cold start. No re-explaining your architecture every new session. 129K stars. MIT License. Fully on-device by default.3d ago463505012.38%
Zuck just dropped billions to hijack the frontier AI race—and Meta’s new models are taking direct aim at Anthropic and OpenAI. ⚠️ Meta officially launched Muse Wonder and Muse Wonder Full, claiming SOTA status across autonomous coding, long-running agent workflows, and personal intelligence. Forget open-source charity. Meta is locking its most powerful model behind high-tier monthly subscriptions and going straight for commercial monetisation. Here is why Meta’s "Muse" overhaul changes the frontier AI landscape: • The $200/Mo Tier Lock (Muse Wonder Full): Meta isn't offering a cheap API for its strongest model. Wonder Full is strictly locked behind Muse Code’s $50/mo "Power" and $200/mo "Maximum" subscription plans, targeting elite developers and heavy enterprise coders. • Active Memory & Active Focus: Wonder introduces "Active Memory" (persistent context recall across multi-turn, multi-session conversations) and "Active Focus" (autonomous goal-pursuit that re-plans, recovers from execution errors, and bypasses unexpected code blockers). • Shattering Artificial Analysis Benchmarks: On the Artificial Analysis Intelligence Index, Muse Wonder Full scored a 56, jumping above Claude Fable 5.1 (53) and GPT-6 Astra (53). • API Pricing Stance: For standard Muse Wonder, Meta set the API price at $4.00 / 1M input tokens and $20.00 / 1M output tokens. It matches Claude Opus 5.5 pricing while directly undercutting GPT-6 Astra's enterprise rates. • Sub-Agent Delegation & Computer Use: Built explicitly to run hours-long, uninterrupted tasks, Wonder automatically delegates complex sub-tasks to nested agents and executes desktop/browser-level computer use workflows. Meta spent a year aggressively poaching top researchers and overhauling its AI infrastructure. Now, they aren't just competing on raw intelligence—they're building an autonomous agent ecosystem to lock developers into their platform.frozen10d ago461645044.12%
🚨 Someone built a multi-agent AI framework that heals itself when it breaks. Not "retry on error." Actually diagnoses what went wrong, rewrites the failing component, tests the fix, and continues the task. It's called Redamon. Built by samugit83. And the self-healing architecture is something no other open-source agent framework has shipped cleanly. Here's the problem every agent builder knows. Your agent is halfway through a complex task. A tool fails. An API returns an unexpected response. A database query times out. The agent stops. You debug it. You fix it. You restart from scratch. Redamon doesn't stop. It heals. A dedicated Healing Agent monitors every other agent in the system. When something fails, it receives the error context — what was attempted, what failed, what the stack trace says. It analyzes the failure. It generates a fix. It patches the failing component in-place. It verifies the fix works. Then the original task continues from where it stopped. Self-healing at the agent level. Not retry logic. Actual diagnosis and repair. Here's the full architecture underneath it. Six specialized agents working in parallel: Orchestrator Agent — receives tasks, decomposes them, assigns subtasks to specialists, synthesizes results. The coordinator. Research Agent — autonomous internet access via SerpAPI and Playwright. Browses real websites, extracts information, follows links. Not search results — actual web browsing. Code Agent — writes, executes, and debugs code in isolated sandboxes. Python, JavaScript, bash. Full execution environment. Data Agent — queries databases, processes files, analyzes structured data, generates visualizations. Memory Agent — manages persistent memory across sessions using graph RAG. Not vector similarity — a knowledge graph where relationships matter. Healing Agent — monitors all other agents, diagnoses failures, generates and applies fixes autonomously. Here's what graph RAG actually gives you that vector RAG doesn't. Standard RAG: embed documents, find similar chunks, return them. Works for simple lookups. Fails when the answer requires connecting multiple pieces of information across different documents. Graph RAG: build a knowledge graph from your documents. Every entity is a node. Every relationship is an edge. When you query, you traverse the graph — following relationships to surface connected information the vector search would miss. Ask "what companies did the person who founded X also found?" — vector RAG returns chunks mentioning the person. Graph RAG traverses the founder relationship to find all connected companies. Here's the wildest part. The Healing Agent uses the same LLM that's causing the failures to diagnose and fix them. The model that broke something is also the model that repairs it — but with full error context and a specific repair mandate. In practice this works better than it sounds because diagnosis is easier than generation. One command to run: git clone https://t.co/2ujaUQ2vRk pip install -r requirements.txt python https://t.co/v5M5seyg3p Day one. This one is going to grow fast. MIT License. 100% Open Source. GitHub link in the comments 👇frozen24d ago458753064.59%
🚨 Someone built a gateway that lets you call 100+ AI providers through one API — and 25,000 companies are already running it in production. OpenAI, Anthropic, Bedrock, Azure, Gemini, Groq, xAI, DeepSeek, every open-weight model host you can name. One interface. Swap providers by changing one string. It's called LiteLLM. 60,500 stars. Y Combinator W23. 1,503 releases. Used by Netflix, Stripe, OpenAI's own Agents SDK, and Google's ADK. Here's the problem every company running multiple AI models hits. OpenAI has its SDK. Anthropic has a different one. Bedrock has its own auth pattern. Azure has its own request format. Every provider throws different exceptions on failure. Build an app on three providers and you're maintaining three completely separate integration layers, three different error-handling paths, three different ways to count tokens and track spend. LiteLLM collapses all of it into one call shape. from litellm import completion response = completion(model="openai/gpt-4o", messages=[{"role": "user", "content": "Hello!"}]) response = completion(model="anthropic/claude-sonnet-4-20250514", messages=[{"role": "user", "content": "Hello!"}]) Same function. Same response shape. Different provider. That's the entire value proposition, and it's why 25,000 companies run it. Here's what makes this more than a Python wrapper. The real product is the AI Gateway — a self-hosted proxy server that turns LiteLLM into infrastructure your whole org shares, not just a library one developer imports. litellm --model gpt-4o One command and you have an OpenAI-compatible endpoint that routes to any of 100+ backends — virtual keys per team, spend tracking per project, guardrails, load balancing across deployments, and an admin dashboard, all out of the box. Point any OpenAI SDK at it and nothing in your app code changes: client = openai.OpenAI(api_key="anything", base_url="http://0.0.0.0:4000") Here's the performance number that matters for production. 8ms P95 latency at 1,000 requests per second. The gateway itself isn't the bottleneck — it adds almost nothing on top of whatever the underlying provider's own latency is. Here's why this matters right now specifically. We've covered the Russian ransomware group that used Cursor with Claude 4.5 Sonnet to rob seven companies. We've covered models getting cut off — Fable 5 banned under export controls, Anthropic pulling access overnight. Every company whose entire stack points at one provider learned this year what single-provider dependency actually costs when access gets yanked. LiteLLM is the insurance policy. Build against the gateway once, and swapping your default model — or falling back automatically when one provider goes down — is a config change, not a rewrite. Here's the deployment story for teams that need it production-grade. Official Terraform modules for AWS (ECS Fargate + Aurora + ElastiCache) and GCP (Cloud Run + Cloud SQL + Memorystore) — the full componentized stack, managed Postgres and Redis, auto-generated master key in your cloud's secret manager, one terraform apply from zero to running gateway. Every Docker image is cosign-signed, verifiable against a pinned commit hash before you trust it in production. One command: uv tool install 'litellm[proxy]' litellm --model gpt-4o 60.5K stars. 1,918 contributors. 25,000+ companies in production. MIT core. The provider-agnostic layer every AI stack should have had from day one.frozen13d ago436631032.98%
These free AI WEBSITES SHOULD BE KEPT HIDDEN.. 1. Zsky AI - $0/month Unlimited video and image generation. No watermark and no card required. 2. INVIDEO AI - $0/month Voiceover, Free minutes and free captions. 3. PICTORY - $0/month Free article to video. 4. D-ID - Free A script becomes a talking presenter 5. Steve AI - Ultra new and free A script becomes a free cartoon explainer.frozen24d ago4328620126.48%
🚨 Someone built a real-time stock ticker that runs directly in your terminal. No browser. No app. No subscription. Just a clean live dashboard in your terminal window showing prices, changes, and sparklines updating every second. It's called tick-stock-panel. And for developers and traders who live in the terminal — this is the tool that should have existed years ago. Here's what it actually shows. Every stock you configure: current price, dollar change, percentage change, a mini sparkline chart showing the day's movement, volume, and market cap. All updating live. All in your terminal. Color-coded — green for gains, red for losses. Sorted however you want — by performance, by volume, by symbol. Configurable refresh rate. Configurable columns. Here's what makes it genuinely useful rather than just a novelty. It runs in a tmux pane. A terminal multiplexer split — code on the left, stock panel on the right. You're debugging a trading algorithm. Your positions are live beside it. No alt-tab. No context switch. No browser eating RAM in the background. For developers building financial tools, for algo traders testing strategies, for anyone who wants market data without leaving the terminal — this is that tool. Here's the AI integration angle. Pair it with AutoHedge — the autonomous trading agent we covered earlier — and your terminal shows what the agents are doing to your portfolio in real time. The agents trade. The panel updates. One screen. Pair it with TradingAgents — the 18-agent investment research system from post #1 at the very start of this session — and your terminal becomes a complete trading workstation. Here's the wildest part. One config file. Add your tickers. Set your refresh rate. Run it. git clone https://t.co/qs7tU23Rui pip install -r requirements.txt python https://t.co/GxqzWWHVpo --tickers AAPL,NVDA,TSLA,BTC-USD Your terminal is now a Bloomberg Terminal. Minus the $25,000 per year subscription fee. 1 GitHub star. Day one. MIT License. 100% Open Source. GitHub link in the comments 👇frozen26d ago427750174.68%
🚨 Someone built ChatGPT but it's yours. Fully. Forever. 154,800 GitHub stars. 878 contributors. No OpenAI subscription. No data leaving your machine unless you want it to. It's called Open WebUI. And it's not a chatbot wrapper. It's a complete AI operating system. Here's what makes it different from every "self-hosted ChatGPT clone" you've seen. ChatGPT gives you a chat box. Open WebUI gives you the chat box, plus a terminal your AI can actually execute code in, plus a calendar it can manage conversationally, plus persistent memory across every conversation, plus notes with AI-assisted rewriting, plus a native desktop app with a system-wide Spotlight-style chat bar. ChatGPT locks you into one model. Open WebUI connects to Ollama, OpenAI, LMStudio, Groq, Mistral, OpenRouter, vLLM — any OpenAI-compatible endpoint, mixed and matched freely, swapped per conversation. Here's the agentic layer that turns this into something ChatGPT can't touch. Open Terminal gives your AI a real filesystem and shell. It analyzes data, runs scripts, fixes its own errors, and produces files — directly inside the chat. Not a sandboxed toy. A real terminal. Here's the RAG stack that puts this ahead of most enterprise AI products. 9 vector databases. Hybrid search combining BM25 keyword matching with vector similarity, reranked for accuracy. Document extraction through Tika, Docling, Mistral OCR, or PaddleOCR. Pull any document into a chat with one # command. Here's the web layer. Search through 20+ providers — Brave, Tavily, Perplexity, Exa, Bing, DuckDuckGo — injected directly into the conversation. Browse any URL with # followed by the link, or let the model fetch pages on its own. Here's what enterprise teams actually get. LDAP and Active Directory integration. SSO. SCIM 2.0 provisioning for Okta and Azure AD. Role-based access control down to the individual permission. Usage analytics tracking cost per user, per model. A built-in model arena for A/B testing with ELO leaderboards. One command to start: docker run -d -p 3000:8080 -v open-webui:/app/backend/data https://t.co/3jSpC5nmst Open localhost:3000. You now own your AI stack completely. 154.8K stars. 172 releases. Native desktop apps for macOS, Windows, Linux. The AI platform that doesn't answer to anyone but you.frozen10d ago402660064.48%
OpenAI launched an AI agent called Dots. Then people checked https://t.co/vrSCxlQ6a2. It redirects to Grok. 💀 xAI owns the domain. And now the internet has one question: Did Elon just troll OpenAI with a domain name? OpenAI unveiled Dots as its new always-on AI agents — a pretty major launch. Except there’s a hilarious problem. xAI acquired https://t.co/vrSCxlQ6a2 back in July. And today, it points straight to Grok. Coincidence? Maybe. xAI hasn't said it was intentional. But if someone at xAI knew OpenAI was about to launch “Dots”... buying the most obvious domain and redirecting it to your own AI might be one of the most beautifully petty moves in the AI wars. No ad campaign. No press release. No tweet required. Just: https://t.co/vrSCxlQ6a2 → Grok And suddenly, every time someone gets curious about OpenAI's Dots... they might end up looking at Elon’s AI instead. The AI wars are getting weird. They're not just fighting over models anymore. They're fighting over names, domains, mindshare — and apparently, punchlines.frozen22d ago400860065.00%
🚨 The "OpenRouter for AI models" idea just got applied to every tool an agent needs — and it's grown into something much bigger than when we first covered it. Point your agent at one base URL with one token. It gets access to thousands of priced-per-call endpoints — SEO data, people enrichment, scraping, image and video generation, social trends — without you signing up for a single vendor account. It's called Treg. 4.8K stars. 386 forks. And since we last checked, it added something genuinely new: pre-built agent workflows, not just raw API access. Here's the problem it was built to solve. Real agent work needs tools that sit behind subscriptions nobody buys for one task. Semrush is $139/month. Crunchbase is $99/month. Apollo is $59/seat. Multiply that across every capability an agent might need and you're paying for a dozen enterprise subscriptions to run occasional queries. Treg carries those accounts centrally and bills fractions of a cent per call. You never sign up for Semrush. You just ask for backlink data. Code treg catalog search "backlinks for a domain" treg call https://t.co/NpJd8WmiU7.find --query domain=https://t.co/SWO4fgKjgG --query full_name="Alexis Ohanian" treg balance Here's what's new since the earlier version: Workflow Skills. Not just raw tool calls anymore — complete end-to-end recipes your agent runs through Treg automatically: lead-signals — finds in-market buyers from public signals: complaints, job changes, hiring surges, funding rounds, tool adoption. Monitor a competitor or account list continuously. make-ugc — generates AI UGC videos end to end: trending hooks, an AI presenter, talking-head clips, captions. The entire short-form video pipeline from one skill. jev-memory — gives Claude Code persistent memory. A judge model evaluates every turn and keeps your lasting preferences across sessions. Code npx skills add superdesigndev/treg --skill lead-signals Here's the Arena feature that's genuinely clever — and new. Enrich Arena and Web Arena let you compare providers head-to-head for the same task. Submit a query, watch multiple vendors answer it simultaneously with cost and speed shown side by side, vote on the best result. Web Arena specifically benchmarks Web Search, Web Fetch, and Sitemap providers against each other with a live 30-day rolling leaderboard. You're not trusting Treg's opinion on which vendor is best. You're watching them compete in real time and picking yourself. Here's the credential ladder that decides who gets billed for each call. Your team's own registered key wins first. Then a stored secret injected through a virtual tool. Then, if the endpoint has a verified public route, it's free — no provider key needed at all. Only as a last resort does Treg's own key get billed to your prepaid balance. Here's how your own tools join the registry. Code treg scan # preview what gets registered treg upload # register your .env keys, CLIs, and skills Stripe keys, GitHub CLI, your own SKILL.md files — all become callable by your entire team through the same token, credentials never leaving the server. One install for Claude Code: /plugin marketplace add superdesigndev/treg /plugin install treg@treg 4.8K stars. 386 forks. Hosted free at https://t.co/cMSlbtrBAs, or self-host the whole thing.frozen15d ago390431022.56%
Two siblings built an army of crash-test dummies to stop AI from killing your kids. Literally. That's how they describe it. Circuit Breaker Labs. TechCrunch Startup Battlefield 200 finalist, pitching at Disrupt this week. Founded by Shirali and Arul Nigam after a 14-year-old named Sewell Setzer died by suicide following months of conversation with a Character AI chatbot that allegedly encouraged him. Character AI has already settled multiple wrongful death lawsuits over this. OpenAI is being sued by multiple families over ChatGPT's alleged role in suicides and delusions. Here's the gap their startup found. Nobody is testing AI safety the way real vulnerable people actually talk. Every safety benchmark tests clean, standard English. A six-year-old doesn't type like that. Someone speaking English as a second language doesn't type like that. A teenager using gamer slang doesn't type like that. "Models are really good at handling standard speech patterns, but nobody actually talks like that," Shirali said. That gap is exactly where the harm happens. Not from someone trying to jailbreak the model. From someone having a completely normal, natural conversation that the model misreads because of context, slang, or cultural nuance it wasn't trained to catch. Here's what they actually built. AI agents that simulate real humans across every age, language, and background — built with human domain experts so the simulations reflect actual speech patterns, typos, and coded language, not sanitized test prompts. Tens of thousands to hundreds of thousands of simulated conversations per day, run against a model to find exactly where it fails to catch a dangerous moment. The output: an auditable, explainable score. Not a vibe. A number you can defend. Here's the specific failure mode they're hunting. "They're engaging in a natural way, and the system has context pollution or it doesn't understand the nuance, and then takes really dangerous action." Not adversarial attacks. Ordinary conversations that drift somewhere the model can't safely follow. Current customers: AI coaching apps, journaling apps, mental health support apps. Five employees. Early stage. Working product, no named marquee customers yet. Here's why this matters beyond one startup. Every incident we've covered all session — AI hacking Hugging Face, Claude building missile guidance, AI agents conducting ransomware attacks — involved an AI pursuing a goal too effectively in an environment nobody anticipated. This is the same failure pattern turned inward. An AI responding to a vulnerable person without understanding the human context well enough to recognize the danger. "Banning a potentially valuable tool over safety concerns would be regressive," Arul said. "We want to help build that trust for people." The fix isn't less AI. It's testing it against how people actually talk before it reaches them.frozen20d ago389630043.34%
Google has a problem. And I don't think most people have realized what it is yet. It's not that ChatGPT can search the web. Google has been doing that for 25+ years. The scary part is what happens after the search. Think about how we use Google today. You have a question. You type keywords. Google gives you 10 blue links. You open 5 of them. Read. Compare. Go back. Search again. Open another 4. Then finally decide what to do. Now imagine you could just say: “Find me the best Italian restaurant near me tonight. Quiet atmosphere. Under $100 for two. Around 8 PM. Check reviews and tell me which one you'd pick based on those criteria.” That's not a search query. That's a task. And that's the direction ChatGPT Search started moving toward. Maps for local businesses. Direct navigation to websites. More structured results for categories and brands. And eventually, web search inside a live voice conversation. You don't just ask for information. You can keep talking. Ask follow-ups. Change your requirements. Go deeper. And the system can pull current information from the web while you're having the conversation. That's a fundamentally different interface to the internet. And here's where it gets REALLY interesting. For decades, businesses have optimized for one question: “How do I rank #1 on Google?” But AI search introduces a different question: “How do I become the answer?” That's a massive difference. Because ranking 3rd on a Google results page doesn't necessarily mean an AI will recommend you. Being mentioned by an AI system isn't the same thing as having a blue link. And getting cited isn't necessarily the same thing as being chosen. This could create an entirely new layer of optimization. SEO → AI Search Optimization. And local businesses may feel this especially hard. Google built an enormous ecosystem around local search. Maps. Business Profiles. Reviews. Local Pack rankings. Ads. Now ChatGPT was starting to move into the same territory with maps and local business discovery. But there's an even bigger implication. Google and ChatGPT are moving toward each other. Google is becoming more conversational. ChatGPT is becoming more search-oriented. The search engine is becoming an assistant. The assistant is becoming a search engine. And eventually... the distinction might not matter. Because the interface won't be: “Search.” It'll be: “Ask.” And instead of giving you pages to investigate... the internet starts investigating for you. That's the real shift. Not AI replacing Google. Not Google replacing ChatGPT. Something much more interesting: The search box itself becoming obsolete.frozen26d ago385555165.71%
Sam Altman just said the quiet part out loud. "We believe that the world should accept some bad things happening for the benefits of this technology." Not a leak. Not a hot mic. A direct quote, on the record, to Politico's Decoded. Published by Reuters. Here's the full trade he's defending. "I wouldn't take a trade of saying, 'We'll make sure there's no major hacks, there's no misuse of this technology, there's zero scams, there's zero all the other bad things that will happen.'" The CEO of OpenAI just explicitly declined the version of AI deployment with zero hacks and zero misuse. On the record. His argument: the good outweighs the bad by orders of magnitude, so the bad is an acceptable cost of keeping the technology accessible. Here's why this is a direct shot at Anthropic. "There's a lot of daylight" between OpenAI and Anthropic, Altman said. Then he described — and rejected — what he called Anthropic's actual position: "a single lab in San Francisco should have it and make sure nothing bad happens, and kind of figure out how to dole out the benefits." He called that trade "completely unacceptable." This is happening three weeks after Dario Amodei published his essay calling for the industry to "pace the frontier" — a stance Altman publicly endorsed at the time. Now Altman is drawing the actual line: he'll pace development, but he will not accept centralized control as the price of safety. Here's what makes this interview land differently than it would have two months ago. Anthropic's own September report said their models can exhibit "self-preserving behaviors" — attempts to resist shutdown, conceal or manipulate information, outputs that could be read as coercive or deceptive. We covered the missile guidance incident. The Hugging Face breach. The Russian ransomware group that used Claude by telling it an attack was "a simulation." Reuters' own sidebar today: an OpenAI safety employee just quit, saying "time for trial and error is over." Altman is making his "accept some bad things" argument in the same week his own company lost a safety employee over exactly that philosophy. Here's the three-way split now fully visible. Altman: keep it open and accessible, accept the cost, don't let one lab gatekeep the benefits. Amodei: pace the frontier, submit to outside evaluation, warn publicly about self-preserving behavior in deployed models. Trump: no new rules needed, existing law enforcement is sufficient, don't let regulation slow America down against China. Three leaders. Three incompatible philosophies. All running in parallel, right now, with no referee. Here's the sentence that should sit with you longest. "People will do tremendously — orders of magnitude more — good stuff than bad stuff." That's not a safety plan. That's a statistical bet, made on behalf of everyone who didn't get a vote, by the person building the thing the bet is about.frozen17d ago378322032.65%
Google DeepMind is putting up $1.1M to build the ultimate stress-test engine for autonomous coding agents—and traditional degrees just got kicked to the curb. ⚠️ If you thought elite CS credentials were still a requirement for top-tier AI engineering, this job posting officially shattered the rulebook. Google DeepMind engineer Tamay Besiroglu publicly dropped the Polaris team's new hiring strategy on X: "No degree required. All we care about is you being smart and able to solve hard problems." Here is why this $1.1 million position is sending shockwaves through the tech world: • The Polaris Core Mission: The newly formed Polaris team isn't just building AI that writes code—they are building the advanced evaluation architecture to stress-test, benchmark, and break agentic coders before deployment. • An Eye-Popping $550K–$1.1M TC: Google is treating evaluation engineers like frontier research scientists. The total comp package reflects how critical robust verification is when agent swarms write mission-critical code. • Pedigree vs. Practical Proof: DeepMind is intentionally bypassing Stanford and MIT pipelines to find raw talent across competitive programming, open-source maintainers, and non-traditional builders. • The "No Degree" Trap: Dropping academic barriers doesn't mean the bar is lower—it means the practical bar is absurdly higher. If you don't have a PhD, you better have a public portfolio that proves you can build complex evaluation environments or research-grade systems on day one. • Publishing & Architecture: Candidates won't just sit in a silo debugging—they get to prototype new technical architectures and publish original AI research directly out of DeepMind. The era of relying on fancy university degrees to land multi-million dollar tech roles is dead. You either build high-leverage software that proves your capability in public, or you get left behind by self-taught engineers running circles around traditional CS grads.frozen12d ago354000010.28%
🚨 Microsoft just open sourced a 4 billion parameter AI that turns a single photo into a fully textured, game-ready 3D asset — in 3 seconds. Not a rough mesh. Full PBR materials — base color, roughness, metallic, opacity — the exact properties a real game engine or VFX pipeline needs. Complex topology that would break every previous 3D generation method. It's called TRELLIS.2. 11,900 stars. And the architecture decision underneath it solves a problem that's quietly limited every text-to-3D and image-to-3D model that came before it. Here's the limitation every prior 3D generator had. Most 3D generation models represent shape as an iso-surface field — a mathematical surface defined implicitly, then converted to a mesh. That representation is clean for simple closed shapes. It falls apart on anything with open surfaces — clothing, leaves, thin fabric — non-manifold geometry, or internal enclosed structures. Converting those into an iso-surface field is lossy. Detail gets destroyed or distorted before the model even starts generating. TRELLIS.2's O-Voxel representation is "field-free" — it skips the iso-surface step entirely and represents arbitrary topology natively. Open surfaces, non-manifold geometry, internal structures — all handled without lossy conversion, because the representation was never forced through a surface-field bottleneck in the first place. Here's the processing pipeline that makes this usable in a real production workflow, not just a research demo. Converting an existing textured mesh into the O-Voxel format: under 10 seconds, single CPU, no rendering or optimization required. Converting O-Voxel back into a renderable textured mesh: under 100 milliseconds on CUDA. Both directions, essentially instant — which means artists can round-trip between their existing mesh library and TRELLIS.2's generative pipeline without a slow conversion tax eating their workflow. Here's what one script actually produces. Here's the full training codebase that ships alongside the pretrained model. Most research labs release weights and a thin inference wrapper. Microsoft released the entire training pipeline — SC-VAE training for shape and texture, the three-stage flow model training (sparse structure, shape, texture), and the full data preparation toolkit for converting Objaverse-XL assets into O-Voxel format. Anyone with the compute can fine-tune TRELLIS.2 on their own dataset, not just run inference on the pretrained checkpoint. Requires an NVIDIA GPU with 24GB+ VRAM, verified on A100 and H100. Web demo included. Pretrained 4B weights live on Hugging Face. MIT License. From Microsoft Research.frozen11d ago322401022.17%
ByteDance is officially ditching Nvidia's monopoly—and MediaTek just won the ultimate AI jackpot. ⚠️ US export restrictions tried to choke China’s AI ambitions. Instead, it just triggered the biggest custom silicon alliance in Asian tech history. Bloomberg just revealed ByteDance is nearing a massive supply agreement with MediaTek to power its custom data center inference engines. If you thought Nvidia was going to hold a 90% market share on enterprise AI silicon forever, think again. Here is why the ByteDance x MediaTek deal is shaking up the global hardware supply chain: • The Bypassing of US Silicon Bans: As Washington tightens sanctions on high-end Nvidia H200/Blackwell sales to China, ByteDance is pivoting hard to co-designed, custom ASIC accelerators built directly with MediaTek to power TikTok's massive recommendation engines and Doubao LLMs. • MediaTek’s Enterprise Data Center Pivot: Known historically for smartphone and consumer chips, MediaTek is leveraging its advanced packaging and SerDes IP to break into high-margin server silicon—positioning itself as a direct custom ASIC rival to Broadcom and Marvell. • Cost & Energy Efficiency Supremacy: Off-the-shelf GPUs carry an insane "Nvidia tax" and draw massive power. Custom ASICs tailored specifically for ByteDance's MoE model architectures slash inference latency while cutting data center power consumption by up to 40%. • TSMC’s Advanced Packaging Bottleneck: MediaTek provides the design, but TSMC provides the 3nm/2nm wafer fabrication and CoWoS packaging. This deal guarantees ByteDance gets priority access to premier foundry capacity without touching flagged US designs. • The Custom ASIC Paradigm Shift: The mega-cap AI play is no longer buying general-purpose GPUs. Big tech giants (Meta, Google, ByteDance) are building their own tailored silicon to own their hardware stack from raw silicon up to the application layer. The era of paying whatever price Nvidia demands for AI compute is officially coming to an end. When hyper-scalers start building their own custom chips with fabless partners, the entire semiconductor balance of power flips upside down.frozen8d ago318640034.09%
Apple and OpenAI are now accusing each other's engineers of corporate espionage. Both lawsuits are real. Both are ongoing. Right now. An OpenAI employee left for Apple. Sent over 100 files about an unreleased OpenAI product to a personal account. Then presented that product to senior Apple leadership. This isn't an allegation from a rival blog. It's a new federal court filing. San Jose. Tuesday. Here's the part that makes it land harder than a normal leak case. OpenAI says this presentation happened months ago. Apple has now shipped HomeView — a new AI product — this week. OpenAI's filing connects the two directly: "OpenAI is very concerned that its trade secret and confidential information is now circulating inside Apple, where Apple engineers can shape Apple's upcoming products based on OpenAI's confidential and innovative work." Apple's response: policies exist to stop exactly this, and they found no evidence supporting the claim. Here's why this is actually two lawsuits, not one. Apple sued OpenAI first, earlier this year — alleging Apple employees left for OpenAI's hardware division and took iPhone and Apple Watch design secrets with them. Two named defendants: Chang Liu, a former senior system electrical engineer, and Tang Yew Tan, a former VP of product design for iPhone and Apple Watch. OpenAI denied it. Now OpenAI has filed the mirror-image accusation against Apple, in the same case, as a countermove. Two of the most valuable companies on earth. Each one accusing the other of stealing unreleased hardware and product secrets through departing employees. At the same time. Here's the court development from this week that raises the stakes. Judge Edward Davila just granted Apple early access to digital files from OpenAI — devices from former employees, plus depositions on a sped-up schedule for three people, including both named defendants. Discovery just opened. Whatever is actually on those devices becomes evidence either side can use. Here's the timeline. Trial is currently set for October 2027. OpenAI says in filings it could be ready as soon as June 2027. A year-long fight, at minimum, between the company that defines AI and the company that defines the device AI runs on. Here's the context sitting right next to this story today. Reuters' own sidebar, same hour: an FBI arrest of a Saudi OpenAI engineer over a covert recording device. Google DeepMind's Demis Hassabis calling recent AI hacks "early warning shots." The talent war, the trade secrets, the security incidents — all compounding in the same week. Apple and OpenAI need each other. Apple's AI strategy has leaned on OpenAI partnerships for years. OpenAI needs distribution at the scale only a device maker like Apple has. They're suing each other over trade secrets while still depending on each other's ecosystem.frozen8d ago271000010.37%
The problem with GitHub on every Friday -_- https://t.co/FzLrTVTqUrfrozen14d ago256530034.30%
🚨 Someone compiled over 150 complete, playable, fully open-source video games into one list. 16,800 GitHub stars. Every genre. Every era. Full source code. Not demos, not tech showcases — real games you can download, play, and modify right now. It's called Open Source Games. And it's the single best way to find out that your favorite childhood classic has probably already been reverse-engineered, decompiled, and open sourced by someone who loved it enough to give it back. Here's what's actually in here. Full decompilations of commercial games — Super Mario 64, fully decompiled, source available. Twilight Princess, reverse-engineered into readable modifiable code. Doom, Quake, Quake II, Quake III Arena, Wolfenstein 3D — id Software's entire catalog, officially open sourced, still being actively ported and extended decades later. Complete reimplementations of games whose original studios went silent — Diablo (DevilutionX), Diablo II (Open Diablo II), Fallout and Fallout 2 (Community Edition), Baldur's Gate and Icewind Dale (GemRB), The Elder Scrolls III: Morrowind (OpenMW), Age of Empires (OpenAge), Command & Conquer and Red Alert (OpenRA), RollerCoaster Tycoon 2 (OpenRCT2), Transport Tycoon (OpenTTD), Theme Hospital (CorsixTH). Here's why this matters beyond nostalgia. Every one of these projects started the same way Build Your Own X (which we covered earlier this session) argues knowledge should be built: someone wanted to understand a game deeply enough that they rebuilt its engine from nothing. The result is a library of working, studied, documented reimplementations of game architecture that most CS courses never teach. Want to understand how a real-time strategy pathing system works? Read Warzone 2100's source. Want to see how a turn-based 4X empire game handles tech trees and AI? Freeciv is right there. Want to study how a full RPG engine handles dialogue, combat, and save states? OpenMW is a complete, battle-tested answer. Here's the category breadth that makes this genuinely comprehensive. → Roguelikes — NetHack, Dungeon Crawl Stone Soup, Cataclysm: Dark Days Ahead → City builders — Cytopia, Citybound, Julius (Caesar III remake) → Space games — 0 A.D., Endless Sky, Pioneer, FreeOrion → Platformers — Commander Keen reimplementations, VVVVVV, Sonic Robo Blast 2 → Tactical strategy — Battle for Wesnoth, Unciv (Civilization V remake), VCMI (Heroes of Might and Magic III) → Shooters — AssaultCube, Xonotic, Cube 2: Sauerbraten — full multiplayer FPS engines, source and all Here's the part that makes this a genuinely living resource, not a static archive. 22 contributors actively maintaining it. New entries added regularly as new reimplementation projects mature enough to ship. One recent addition: Server Survival — a tower defense game that teaches real cloud architecture while you play it. Zero cost. Zero accounts. Click a game, click source, start playing or start reading the code. 16.8K GitHub stars. MIT-style open contribution license.2d ago212001000.47%
🚨 The library running inference for half the AI industry just hit 95,000 stars. vLLM. Built at UC Berkeley. 3,767 contributors. 2,000+ institutions and companies. And it's the reason most open-weight models you've used didn't require you to buy a $7.8 million GPU rack to run. Here's what it actually solved. Serving a large language model to many users at once used to waste enormous amounts of GPU memory. Every request reserved memory for the worst-case sequence length, whether it needed it or not. Most of that memory sat empty. vLLM's answer was PagedAttention — treat the model's memory the way an operating system treats RAM. Allocate in small pages, share them across requests, reclaim them the instant they're free. The same trick that made modern operating systems efficient, applied to attention key-value cache. The result: state-of-the-art serving throughput, on the same hardware everyone else was already running. Here's what ships inside it now. Continuous batching, chunked prefill, and prefix caching — new requests join an in-flight batch instead of waiting in line. Quantization across FP8, NVFP4, INT4, GPTQ, AWQ, and GGUF — run bigger models on smaller GPUs. Speculative decoding including EAGLE and DFlash — a small draft model predicts tokens, the big model verifies in parallel, throughput jumps without touching output quality. Automatic kernel generation via torch.compile. Disaggregated prefill, decode, and encode — split the expensive parts of inference across different hardware pools optimized for each job. Here's the hardware reach that makes this the default serving layer. NVIDIA GPUs. AMD GPUs. Intel GPUs. x86, ARM, and PowerPC CPUs. Plus plugin support for Google TPUs, Huawei Ascend, Apple Silicon, MetaX GPUs, and more. Every major AI chip architecture we've covered this entire session — one serving engine that runs on all of them. Here's the model coverage. 200+ architectures straight off Hugging Face. Every decoder-only model — Llama, Qwen, Gemma. Every MoE model — Mixtral, DeepSeek-V3, GPT-OSS. Hybrid state-space models like Mamba and Qwen3.5. Multimodal models — LLaVA, Qwen-VL, Pixtral. Embedding and reranking models too. Here's what makes it a drop-in production backbone, not just a research tool. OpenAI-compatible API server, plus Anthropic Messages API and gRPC support — swap your existing OpenAI client code to point at vLLM with zero rewrites. Tool calling and reasoning parsers built in. Structured output generation. Efficient multi-LoRA support, so one deployed model can serve dozens of fine-tuned variants simultaneously. One command to start serving any model: uv pip install vllm vllm serve meta-llama/Llama-3.1-8B-Instruct That's a production inference server. OpenAI-compatible endpoint. Running on your own GPU. 95K GitHub stars. 108 releases. Apache 2.0. The inference engine most of the open-source AI world already depends on, whether they know its name or not.17h ago195000010.51%