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gippp69

Gipp 🦅

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@gippp69

18 / ai workflows print money / dm open testing @SpaceXSI so you don't have to. @grok @bot

RosterAI Agents & Productivity ToolsAI Model News & Releases
12,151 followers · 511 following · Last polled 3h ago
Perf. Score
68
Rank #80
Value Score
79.5
#57 of 273 priced
Median views
4.5K
p25 2.4K · trailing 7d
Followers
12K
+5.3% 7d
ER (impr.)
4.33%
eng ÷ impressions
ER (foll.)
2.35%
eng ÷ followers
Est. CPM
$11.14
QT-rate basis
WoW views
-83.8%
▼ falling

Rates & value · est. from 4.5K median organic views

underpriced
Quote tweetbasis
$50
$11.14 / 1K views
Post
—
no CPM
Thread
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no CPM
Retweet
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no CPM
Value Score 79.5 · rank #57 of 273 priced$224.72 / 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 (4.5K), not the mean (6.6K) — so a single viral post doesn't inflate a creator's reach. The p25 floor (2.4K) is the level 75% of posts clear.

Recent posts · ranked by views

PostPostedViewsLikesRepostsRepliesQuotesBookmarksER
https://t.co/gTEKJ3i4XUfrozen11d ago335K2552447264700.25%
https://t.co/J4D4H0CIHAfrozen18d ago235K1931429202360.21%
https://t.co/ZlS3sdGQysfrozen
28d ago
167K
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claude + REA can rebuild software features without copying a single line of code opus 5.5 reads what REA finds inside a program, then rebuilds the feature in a separate clean-room setup. here's how to do it: 1. run npx rea-agents setup to connect REA to Claude Code 2. pick a feature and let REA figure out how it works. save the behavior and rules in a spec file 3. start a fresh Claude session with only that spec. rebuild the feature without touching the original code 4. use /goal to run the tests and check 3 intentionally broken versions. all 3 must fail one project recovered 697 functions in just 12 days, with 696 exact matches. the real trick is building tests that catch mistakes before they become part of the finished software.frozen7d ago139K1.8K300115122.0K3.08%
i'm f**king done with Grok Bots waking up 8,640 times a month just to find nothing changed... one routine running every 5 minutes costs roughly $397/month at Grok 4.6 API rates. run it hourly and that drops to $33. same bot, same job, 12x the estimated cost. so here's how i'd structure a 6-bot crew that actually makes sense overnight. [the setup looks like this] 1. chief routes events, not noise 2. scout checks what actually changed 3. quant compares the last saved state 4. risk returns PASS / CUT / KILL 5. exec handles safe, approved actions 6. scribe logs every run and queues drafts the trick isn't making 6 bots work harder. it's stopping them from waking up for nothing. add a cheap change check before every full run and my cost model drops from $397 to $45 (-89%). anything risky at 3 a.m. stays a draft until you're awake. that's the Grok Bot setup i'd actually trust overnight. full breakdown in the article below ↓frozen11d ago111K668557631.2K1.78%
jev + opus 5.5 is one of those setups that makes agent architecture feel weirdly overcomplicated in hindsight. the useful split isn’t “small model vs big model.” it’s deciding which calls actually need generation at all. → jev routes the next worker → scores which context is worth keeping → catches repeated actions before they burn the run → gates risky tool calls and sends uncertainty to review opus still owns the expensive part: planning, writing, coding, synthesis. jev sits around it as the decision layer. that matters because a typical loop can have 11 decision points but only 2 real generation calls. once those are separated, latency and cost stop scaling with every tiny fork in the workflow.frozen26d ago51K493615335882.35%
dots + gpt-6 astra is f**king insane. sam altman just shipped an agent that can keep working after you close chatgpt. this setup turns 5 dots into an always-on launch team. [the part people will miss: each dot owns a job] 1. researcher watches feedback, docs and new signals, then routes what matters. 2. app builder takes repeated requests, builds fixes and hands back a tested PR. 3. launch lead updates positioning and launch assets whenever the product changes. 4. sales lead tracks requirements, checks product docs and prepares the next move. 5. creator turns new interviews and updates into clips, copy and social drafts. same launch. 5 workers. one coordinated system. approvals only when the action actually needs you.frozen19d ago46K258233813501.47%
Grok Bot users: you're paying for 94 out of 96 overnight runs that find nothing. one skill fixes it: /night-filter save it as a custom skill, then split the night between 4 bots: 1. SCOUT checks for changes every 5 min 2. QUANT kills the full run if nothing moved 3. EXEC does the real work 4. RISK blocks anything you can't undo my last night: 96 checks, 2 full runs, 0 irreversible actions. the expensive part was never the model. it was waking up for nothing. save this before your next overnight routine.frozen8d ago35K366306114112.50%
xAI Grok Bot has a f**king brutal 3 a.m. problem: 96 wakeups while you sleep, and nobody is there to approve anything [the overnight rule] give your Grok Bot crew 3 hard stoplines: → SCOUT checks what changed before waking the full team → EXEC handles safe, reversible actions on its own → RISK leaves anything irreversible as a draft background approvals expire after ~10 minutes. your bots can't wait until breakfast. save this before your next overnight run. full guide below ↓frozen10d ago35K295325113132.00%
An Anthropic engineer’s Claude Code rules basically turn Sonnet 5.5 + Opus 5.5 into a self-routing agent stack. this is f**king filthy in the best way... drop the prompt into Claude Code and it handles the annoying part itself: stay on Sonnet while the task is clean, escalate when a real check fails, and hand Opus only the context that actually matters. less babysitting, less dead context, fewer stupidly expensive switches ↓frozen16d ago34K387423815723.09%
this is the most UNDERRATED Grok Bot setup right now 1 router + 4 specialist agents. no polling, no wasted runs, nothing fires until something actually needs action i switched after doing the math: a routine every 5 minutes costs 12x more than hourly. $397 a month vs $33 for the same job → webhooks, Slack, GitHub and email all hit one router → most events get ignored on purpose, zero wasted runs → 4 specialist agents wake up only when there's real work → risky actions stop at a draft before they reach you stop polling every 5 minutes. let events wake your agents. here's the exact setup, send it to your Grok Bot ↓frozen6d ago30K353266943722.76%
JEV ENGINEERING JUST CUT A CLAUDE LOOP FROM $765/MONTH TO $3.02 the expensive part isn’t always writing code. a busy overnight loop can burn ~600 decisions just asking what to do next, whether a command is safe, or if the task is finished. trigger → Jev → allow / stop / wake → Claude only builds when needed at roughly 4,000 tokens per decision, those checks can cost ~$25.50 a night on Fable 5.1. Jev handles the same decision layer at $0.042 per 1M input tokens with output free. over 30 nights that moves the decision bill from ~$765 to $3.02. batch multiple questions into one call and it falls again to about $1.08/month. that’s the part i care about: Claude keeps the expensive work that actually needs intelligence, while Jev handles the hundreds of tiny judgment calls around it.frozen25d ago25K250113912742.27%
grok bot crews are f**king insane. i gave 6 bots one overnight job and woke up to 612 finished tasks, 47 blocked actions, 89 rejected handoffs and 0 irreversible moves made without me. [the setup was basically this 👇] 1. scout scans the feed and builds the candidate list, but it never gets to decide what actually moves forward. 2. quant scores what survives and attaches the numbers, but it cannot resize a position or touch the final action. 3. risk reads the evidence and returns PASS, CUT or KILL. if one number shows up without a source, the entire handoff gets bounced. 4. exec is the only seat that can touch the final action, and even that stays locked until a timestamped PASS is sitting there. 5. scribe keeps the source, read time, evidence and result attached to every handoff, so one made-up number can’t quietly travel through the crew. 6. chief keeps all 5 seats moving in the right order, but never does the research, scoring or execution itself. what surprised me is that the crew got more useful once every bot became intentionally incomplete. they can keep working after i close the laptop, argue with each other, reject bad work and finish hundreds of tasks overnight. but when something can’t be undone, the whole system stops. 6 bots can run the night. the last stopline is still me.frozen17d ago25K261314713002.57%
Grok Bot users: paste this ONE prompt NOW before your routines burn another $350 this month [check first, run only on diff] 1. every 5 min it compares a hash to the last run 2. nothing changed, it skips in milliseconds 3. real change, it drafts a plan and waits for your tap 4. non-urgent stuff waits until 07:00 in the video: 176 checks, 171 skipped, 4 real runs 1. full run every 5 min: $397/mo 2. hourly: $33/mo, but you're up to an hour late 3. every 5 min + check: $45/mo copy this into your Grok Bot: for every routine: hash check before the full run, skip if nothing changed, hold non-urgent diffs until 07:00, drafts before any send. show me the cost before and after, change nothing until I say go. wake up for changes, not for the clockfrozen4d ago21K286275612843.18%
Claude Code creator Boris Cherny just inspired a prompt that turns Sonnet 5.5 + Opus 5.5 into a cost router that actually knows when to switch. this is f**king sharp... drop it into Claude Code: Sonnet handles the clean work, Opus only steps in after a real failure, and the handoff stays tight instead of dragging 300K of dead context with it. full routing guide below ↓frozen15d ago19K343453413874.26%
jev + opus 5.5 turns 4 agent decisions into a much cleaner loop. instead of letting one model control every branch, jev only handles 2 narrow jobs while the host keeps the hard limits. → filters invalid routes before selection → chooses from real provider/model candidates → picks 1 of 4 steps: inspect / implement / verify / answer → rechecks stale decisions before execution opus stays focused on reasoning, coding and synthesis. jev only decides what happens next inside options the app can actually verify. each decision can then be logged, replayed and compared later, turning 1 opaque agent loop into something you can actually inspect.frozen25d ago16K12792801341.91%
Claude Code found $710/mo hiding inside an agent loop that already looked “optimized” /cost-audit 1. feed it the usage log for every task 2. make it flag late Opus escalations, cold-cache rewrites and effort changes 3. let it rewrite the router, handoff and session rules around those failures this run scanned 4,812 calls across 1,000 tasks and found 3 leaks the biggest was late escalation from Sonnet to Opus after the fixes: $2,530/mo → $1,820/mo $710 saved on the same workload Claude Code can audit the system, not just write the code ↓frozen16d ago14K207245601272.92%
Jev builders: the model bill is decided before the model is called /routing 20,000 events/day become: → 14,000 die as noise → 4,700 become logs → 800 earn a draft → 500 go to human review only 4% reaches writing. Jev costs $0.84; the full loop lands at $29.04. optimize the 4% before the writer.frozen21d ago14K15813330932.11%
holy f*ck, i’m convinced Claude Opus 5.5 is getting dangerously close to replacing a motion design studio… what used to take hours of animation, rendering and manual fixes now runs inside one Claude Code session. the secret isn’t a better prompt. it’s a self-correcting studio: → 1,820 frames generated directly from code → 6 automated checks catch errors and broken layouts → failed checks trigger another fix and render → the loop stops only when all counters hit zero no After Effects. no video model. just Opus, a harness and a loop that knows when the work is actually done.frozen14d ago14K256194202033.81%
SpaceXAI's own Grok Bot docs hide a detail that makes a 5-min routine cost 12x more than hourly. steal these 20 rules to keep the speed and drop the bill by 89%: https://t.co/F8h52goBXHfrozen7d ago13K209154902423.99%
OpenAI Dots gets way more dangerous after 5 steps... OpenAI own numbers show boundary flags at 8.6% for 5-task chains and 19.7% for 10. So I built the whole setup around shorter legs instead of one giant autonomous run. LOOKOUT → MAKER → SKEPTIC → RUNNER 5 steps → checkpoint → continue One dot, 4 seats, one memory, hard permission rules. Copy the prompt from the video, then read the full setup + math in the article below ↓frozen21d ago13K166173311042.49%
JEV FOUNDER DIOGO ALMEIDA JUST SHOWED HOW 20,000 AGENT DECISIONS CAN COST $0.84/DAY the waste starts when every incoming event hits a frontier model before the system even knows if it needs real work. event → Jev → ignore / log / draft / human → Grok Bot writes only when needed in his breakdown, 20,000 events at ~1,000 tokens per decision cost just $0.84 on Jev. only 4% escalate, but those 800 drafts add another $24. push the same setup to 200,000 events and Jev rises to $11.76 while writing jumps to $240. that’s the part i care about: Jev makes decisions cheap, Grok Bot handles the writing, and escalation rate becomes the number that actually controls the bill.frozen26d ago12K177233601092.90%
Opus 5.5 being “2x more expensive” is one of those facts that dies the second you run a long agent loop [the loop changes the math] 1. at 20K context, Opus is still 1.88x Sonnet per turn 2. at 150K, that falls to 1.49x. by 400K, it is only 1.26x 3. the reason is cache. both models pay the same $0.20 / 1M for cache reads, so old context stops widening the bill 4. the ugly part is switching late. drag 300K of failed context into Opus and you spend $1.50 just rebuilding cache 5. catch the failure around turn 6 and hand off 20K instead. that costs about $0.10 at 150K, 30 Sonnet turns cost $1.76 20 Opus turns cost $1.75 the real optimization is not “use the cheaper model” it is knowing when to stop paying for the wrong one ↓frozen13d ago12K208195501133.34%
JEV FOUNDER DIOGO ALMEIDA JUST SHOWED HOW 5 CHEAP DECISIONS CAN STOP GROK BOT FROM WASTING FULL MODEL CALLS letting a writing model decide everything is expensive before it even knows whether the event needs writing. Jev moves that control step in front. signal → harness → Jev → deterministic / Grok Bot / human → tests → logs → next turn jev asks 5 bounded questions first: route, urgency, evidence, safe to send, keep or drop. grok bot only enters when one of those decisions says generated text is actually needed. simple work stays deterministic, risky paths go human, and grok bot gets a smaller context with one clear job instead of the entire desk state. that’s the part i care about: Jev becomes the control layer, Grok Bot becomes the writing layer, and the expensive model stops deciding when it should be expensive.frozen25d ago12K168263811263.05%
This tiny pink spider web just traced how 20,000 agent events turn into only 800 writing jobs. no full model on every signal. Jev decides first, then generation only happens when needed. 20,000 events → 14,000 ignored → 4,700 logged → 800 drafted → 500 reviewed. following the spider makes the cost split obvious: Jev handles the decision layer for $0.84/day. full setup below shows where the expensive calls finally enter ↓frozen20d ago11K154163311072.79%
Mark Zuckerberg (Meta CEO) has engineers building an AI second brain with 200+ knowledge files that cuts token usage by 80%. It's f*cking unreal... Shared memory, self-improving agents, and a system that actually learns from mistakes. Study the Meta setup below, then read the full guide to 10 production AI agent architectures.frozen12d ago10K171152501773.74%
SpaceXAI engineers built these bot templates. i chained 9 of them into a startup team that finds, kills or builds a product every week absurdly f*cking smart... one paste and your Grok Bot will: 1. scout trends on X, Reddit, HN and GitHub every Monday 2. mine real user complaints and map every competitor 3. hold a board meeting and vote GO, HOLD or KILL 4. write the SPEC and build it while you sleep and that's just week one... send this to your Grok Bot and thank me later. full breakdown ↓frozen5d ago9.8K196245001874.66%
GPT-6 ASTRA + CODEX + CHATGPT WORK + 6 DOTS on one spider, and your deal ops basically run themselves... one team tracks every sponsored deal until it’s paid: inbox, Slack, invoices, bugs and approvals all move without opening a new chat every time. [the part people will miss: each dot owns one job] 1. lookout reads brand emails, catches new briefs and sends the useful details to keeper. read-only, nothing goes out. 2. keeper owns the live deal table: brand, format, post date, price and status. if a deadline is under 24 hours away with no brief, it hands the problem to scribe. 3. scribe watches Slack deals, drafts the missing reply and waits for your approval before anything gets sent. 4. clerk catches unpaid work and prepares the invoice. in this run it builds the $850 bill, but the send button stays locked. 5. fixer takes product issues into Codex, fixes the bug, writes the test and opens a draft PR. merge still belongs to you. 6. warden runs Auto-review across the risky handoffs: recipient, message, private data, money, passwords and merges. OpenAI tested 16,600 attack emails and 0 got through. same job. 6 dots. one system that keeps moving even when you’re not there. that’s the real shift Sam Altman is selling for $3.33 a day: the agents do the reading, chasing, drafting and fixing, while your entire job collapses into one queue of decisions only a human should make.frozen18d ago8.7K1577351793.21%
holy sh*t. I turned the Opus 5.5 cost mess into one prompt that finds where the money leaks this thing is f**king useful… drop in your agent runs and it catches bloated context, late escalations and bad handoffs checkpoint earlier → hand off less → lower $/pass paste it into Claude Code and clean up the bill ↓frozen12d ago8.6K162123301414.06%
Holy f*ck... Claude Duet just made every chatbot feel like a search bar. → Before: you open a chat and explain everything from zero. → Now: Claude walks up to you first. Anthropic opened it as a research preview: → brings what needs you, skips the noise → keeps working after you step away → asks before acting as you You used to prompt Claude. Now Claude prompts you. setup prompt ↓1d ago7.7K1206321803.12%
xAI engineer Lingxi Li works on Grok Bot, and this 12x routine cost trap is f**king insane... one routine waking up every 5 minutes means 8,640 runs a month. roughly $397 vs $33 hourly at example API rates. i mapped out how to cut that estimate by 89% using change checks, event triggers and overnight approval rules. the full Grok Bot routine playbook is in the article below ↓frozen11d ago7.1K167333011004.67%
jev + picsart turned one prompt into a creative system that decides before it spends instead of letting expensive models guess their way through every step, i split the workflow in two: Jev handles the cheap decisions, Picsart handles the actual generation. here’s exactly how i wired it: step 1 → the brief enters once. every image, clip, audio pass and edit inherits the same campaign direction instead of starting from a blank prompt. step 2 → Jev sits before the expensive work. it decides whether the next stage should run, stop, reuse something finished, or wait for me. step 3 → the decision set stays tiny: allow_subagent, ask_human, stop_retry, reuse_cache. cheap calls first, expensive generation second. step 4 → Picsart takes over the creative layer. 188 models from 34 providers sit behind the graph for image, video, audio and editing. step 5 → every job gets its own stage. one model does not have to carry the brief, image, motion, sound and final polish by itself. step 6 → when review rejects one output, Jev does not blindly request v2, v3, v4. it can stop the retry and change the route first. step 7 → approved work stays alive. reuse_cache can keep 5 finished stages untouched while only the 1 broken branch gets another run. step 8 → human review only appears where judgment matters. i am not approving every tiny action, just the decisions where taste can change the result. step 9 → the cost gap is the fun part: 11 Jev decisions cost $0.0011 here, versus roughly $0.33 through the heavier LLM decision loop. step 10 → once the graph works, the next campaign is not another rebuild. swap the brief, keep the logic, and run the same system again. jev decides what deserves compute. picsart turns that decision into the asset.frozen29d ago7.0K1147290623.04%
Divo, an ex-Microsoft RL engineer, just shared the prompt he uses to make Grok Bot build his whole bot team. reads the repo, picks the fewest bots, starts read-only. this is f*cking clean... paste it into your Grok Bot, fill your details, and let it build the team. full prompt ↓frozen5d ago6.9K127113501284.34%
Jev + Grok Bot turns a noisy agent workflow into a control stack where expensive generation only happens when it actually earns the call… [this setup separates decisions, writing and human review instead of throwing everything at one model] /1 the harness compresses each event into state, permissions, limits and only relevant context /2 jev handles 5 bounded checks: route, urgency, evidence, safe to send and keep or drop /3 grok bot sits behind that gate, seeing only tasks that genuinely need a draft, summary or open-ended reasoning /4 simple work stays deterministic, risky cases go human, and results feed back into tests, logs and next-turn context the interesting shift is simple: stop optimizing the writer first and optimize how often it gets called.frozen24d ago6.2K1012300633.19%
grok bot routines will quietly eat your budget if you set them and forget them same bot. same job. 12x the cost. a watcher firing every 5 minutes wakes up 8,640 times a month. at grok 4.6 api rates that's around $397. the hourly version sits near $33 how to stop the bleed: step 1 → ask your bot what each routine costs you per month step 2 → push anything non-urgent to hourly step 3 → add a change check so an empty wake ends after a single read step 4 → have it ping you only when something actually moved keep the 5-minute speed with a change check and it drops to about $45. that's -89% and nothing gets slower here's a prompt that audits every routine you run, ranks them by cost and adds the change check for you. paste it into your grok bot and see what it cuts ↓1d ago5.8K1377451904.87%
Jev + Grok Bot Is f**king Insane… i built a 24/7 agent desk where Jev handles decisions, Grok Bot writes only when needed, and humans see only the important stuff. 20,000 decisions later: 1. jev routes every event in 60–140ms across ignore, log, draft or human 2. x replies, github issues, telegram spam and on-chain alerts all use the same decision layer 3. anything uncertain fails closed and goes straight to human review 4. 1% of ignored events gets audited so silent mistakes can still be caught 5. grok bot only writes inside the draft lane and never posts automatically 6. one jev decision costs roughly $0.00004 7. 20,000 decisions cost just $0.84 8. writing + audits cost $28.20 9. the full day finished at $29.04 10. jev itself was only 2.9% of the bill, with a 4% escalation rate the part i care about: 96% of events get filtered before an expensive writing model ever touches them. so the real cost knob isn’t llm pricing anymore. it’s how often your system decides it actually needs generation.frozen27d ago5.6K1045302723.79%
A Jev engineer just showed the 10 constraints that separate a real agent brief from one giant prompt dump instead of piling everything into one instruction, the prompt works like an interface: each block owns one job, one constraint and one piece of context. 10 details stood out: step 1 → lock one concrete output. here it’s a complete 75–90 second educational piece, not “make something about Jev” step 2 → separate the learning goal from the deliverable so the agent knows what actually needs to become clear step 3 → define the visual system before generation: materials, typography, texture, framing and logo behavior step 4 → reduce the topic to one cause-and-effect relationship instead of asking the model to compress everything it knows step 5 → map it into 7 scenes where every scene moves the explanation forward step 6 → treat every scene as structured state: purpose, visual, motion, voiceover, sound and transition step 7 → make transitions carry information. objects and diagrams should evolve into the next scene instead of resetting step 8 → lock the output contract: storyboard, voiceover, sound plan, implementation, 1920×1080 and 24 fps step 9 → define operating behavior separately: precise language, technical clarity and continuity over decoration step 10 → keep the system boundary clean: Jev handles bounded decisions while Grok Bot stays downstream for generation what matters here is constraint architecture. strong prompts don’t necessarily tell the agent more. they leave fewer important decisions undefined. ↳ https://t.co/UBjfupVRizfrozen19d ago5.6K13911300874.77%
Send this Jev prompt to any LLM or AI agent it audits your workflow, finds where you’re burning tokens, time and money, then moves repeatable decisions out of expensive model calls in the desk example, 20,000 Jev decisions cost just $0.84. only 4% escalated to writing, but those 800 drafts added another $24 that’s the real alpha: don’t optimize the expensive model first. reduce how often you need to call it at all Jev handles the cheap picks, Grok Bot writes only when needed, and code keeps control of the risky stufffrozen24d ago5.6K1178401744.30%
AN ATLAN ENGINEER JUST SHOWED WHY A PRODUCTION AGENT SHOULD KNOW WHEN TO GIVE UP only 14% of incoming alerts ever reach the agent. instead of making intelligence the default path, Atlan makes it the exception and forces routine work through cheaper deterministic logic first. context → loop → Jev → harness → evals 3 attempts is the ceiling. stay below 50% confidence after that and Python takes the wheel, which turns uncertainty into a designed branch instead of an endless reasoning bill. zoom out and the same logic applies across all 5 layers: shrink what the model sees, gate what deserves reasoning, restrict what it can touch, then replay failures until the weak layer becomes obvious. that’s what makes the architecture interesting to me. the upgrade isn’t giving an agent more freedom, it’s engineering exactly where that freedom begins and ends.frozen24d ago5.5K1216300724.15%
JEV + KIMI K3 CAN CUT A $300 AGENT BILL TO $9.42 BY ESCALATING ONLY 10% OF EMAILS a receipt, a sales pitch and a suspicious invoice shouldn’t all get the same expensive read. jev sorts first, then sends uncertain cases to k3 for a closer look. email → jev → confident: route / uncertain: kimi k3 → unresolved: human for 10,000 emails a month, the budget example gives jev $0.42 and k3 $9 with 10% escalated. each k3 review assumes 1,500 input and 300 output tokens. sending everything to the frontier model comes to $300. move escalation from 10% to 30% and the same estimate becomes $27.42. that’s the number i’d watch: the bill grows with how often jev needs a second opinion. i’d tune the threshold on a week of real emails. getting the review queue small enough to actually use, while still catching the difficult cases, is what makes this worth running.frozen22d ago5.4K1184280663.99%
my friend went to a Grok Bot builders meetup and came back with this one page cost playbook. this is f*cking unreal… his routine every 5 min was burning $397/mo. one rule on this sheet: check first, run only on diff. same routine, now $45/mo. send it to your Grok Bot and ask it to rebuild your routines around it.frozen6d ago5.4K1373431925.13%
Jev + Grok Bot just turned a noisy agent loop into something that actually looks engineered [this setup puts a decision firewall between raw events and expensive generation] /1 Jev reads each event and decides whether it deserves another compute step /2 route, urgency, evidence and safety become bounded decisions before generation /3 noise dies in ignore/log lanes, while only high-value writing reaches Grok Bot /4 ambiguous or consequential cases escape automation and move to human review /5 at 20,000 decisions/day, Jev costs around $0.84 while just 4% escalating adds about $24 the real alpha isn’t cheaper intelligence. it’s making expensive intelligence scarce by design. 👇frozen23d ago5.3K983340613.73%
Grok Bot just got 3x faster. and that's the boring part of the update computer tasks finish 3x sooner, you can tag @bot under any post, and it reads Elon's X Chat once you connect X the part crypto people should care about: step 1 → connect Coinbase, Robinhood or eToro. your whole portfolio lands in one chat step 2 → it goes through your X Chat and splits real deal DMs from "collab?" spam step 3 → anyone asking you to connect a wallet gets flagged before you open it step 4 → tag @bot under any coin post and it pulls the whole thread step 5 → set a routine and it watches all of it 24/7, pinging you only when something moves i wired all 5 into one desk and called it Vault. that's the terminal in the video the result: your bags, your DMs and the timeline in one place p.s. connect X first. the X Chat part is the one nobody's using yet full setup with the prompt in the article ↓frozen3d ago5.2K1227370664.43%
ANTHROPIC ENGINEERS JUST TURNED OPUS 5.5 INTO A REAL OPERATING LAYER claude is usually treated like a chat window. this setup makes it the layer deciding what should happen next across an entire workflow. state → opus 5.5 → tools → code → external systems → human review pricing makes that architecture much easier to run at scale: $4 input, $20 output and just $0.20 for cache reads per 1M tokens. one tester pushed a 680,000-line migration through in under a day, while another hit 39/40 successful load-time fixes across a web app. that’s the part i care about: opus 5.5 starts looking less like an assistant and more like the reasoning core behind long-running research, coding and agent systems.frozen29d ago5.2K997360764.19%
I stopped treating Opus 5.5 like the default “better model” the useful setup is simpler: let Sonnet keep the clean, scoped work, then move only the hard cases when the task actually proves it needs more reasoning this one-page Claude Code guide shows the split, the escalation point, and why a clean 20K handoff can cost $0.10 instead of $1.50 for a bloated 300K switch use it as the routing layer for Claude Code ↓frozen17d ago5.2K12612332824.94%
Sam Altman OpenAI Dots gets way more useful when you stop treating it like one giant agent /digest → /lookout scans 3 sources: email + calendar + repo → /maker turns them into 3 buckets: SHIPPED / MOVED / WAITING → /skeptic checks the chain after 5 steps → /runner ships only after PASS the numbers are the interesting part: 5-step chains: 8.6% flagged 10-step chains: 19.7% flagged 2 shorter legs with checkpoints: 3.4% 4 seats, 1 shared memory, max 5 steps, under 5 approvals/day.frozen20d ago5.0K1117261614.13%
XAI + GROK AGENTS ARE INSANELY CAPABLE UNTIL YOU MAKE GROK DECIDE EVERY TINY THING IN THE LOOP the cleaner setup is to put Jev in front of generation and make every expensive call earn its way in. the shift: routing first, generation second step 1 → collapse messages, repo events, tools and scheduled jobs into one compact event state step 2 → let Jev choose the lane before Grok sees a giant context: ignore / log / write / human step 3 → attach confidence to the route, so uncertainty becomes a real branch instead of another prompt step 4 → kill obvious noise early. in the 20,000-event run, 14,000 events stop before generation step 5 → keep useful state without writing around it. another 4,700 events end as logs step 6 → wake Grok only when language or execution is actually needed. roughly 800 events reach the writing lane step 7 → move risky or unclear cases to human review instead of letting the agent improvise through a guardrail step 8 → keep the route, confidence and handoff visible so the whole desk can be inspected after the run where it gets interesting: Jev handles the decision layer for about $0.84/day in this setup. generation pushes the full loop to roughly $29.04/day. Grok still does the expensive work. Jev just makes sure it only happens when there’s actually expensive work to do. full 5-page breakdown below ↓frozen19d ago4.9K12613281945.36%
TYPE SAFE AI ENGINEERS JUST TURNED 20,000 DAILY AGENT DECISIONS INTO A $0.84 LINE ITEM frontier models are expensive at the exact moment when the system still hasn’t decided if an event deserves real work. Jev sits in front and makes that call first. event → Jev → ignore / log / draft / human → writer only when needed at 1,000 tokens per decision, 20,000 events cost just $0.84 on Jev. only 4% escalate, but those 800 drafts add another $24.00. scale the same desk to 200,000 events and Jev rises to $11.76 while writing jumps to $240.00. that’s the part i care about: Jev makes watching everything cheap, while the escalation rate becomes the number that actually controls the bill.frozen27d ago4.8K1028331754.53%
jev + grok bot is one of those setups that makes agent economics look obvious in hindsight. useful split isn’t “cheap model vs expensive model.” it’s deciding whether an event deserves generation at all. → jev answers lane, urgency and evidence in parallel in 100ms → code guards push low-confidence or missing-evidence cases to human review → ignore gets a 1% audit, log stays ledger-only, draft is the only lane that reaches grok bot → grok bot writes, never routes, while risky cases stay outside automation at 200,000 events, jev costs $11.76 while writing reaches $240. a 4% escalation rate is enough for generation to dominate spend. my takeaway: once decisions become cheap, attention becomes the budget. scaling means keeping writing rare instead of endlessly hunting for a cheaper writer.frozen23d ago4.7K1054331594.31%
i made the chrome dino game multiplayer. it's called dinorunner everyone online races live. 6 zones, daily leaderboard built it while writing about why opus 5.5 is 1.26x, not 2x https://t.co/7VtQfZrfEt https://t.co/e3K9PHNClpfrozen15d ago4.6K11115421645.05%
Holy sh*t, @bot just did my After Effects job No keyframes, no timeline, no plugins. One message and a full 3D motion video was ready Ask it to animate your own terminal and see what comes back The full launch playbook with all 7 bots is in the article below ↓ https://t.co/LjGhRMMzx09h ago4.5K1064311644.58%
Boris Cherny, the creator of Claude Code, just published how he actually uses it... i turned his workflow into 1 prompt that runs a whole marketing agency. no team, no VA here's the full setup step by step: step 1 → paste the prompt from the images into Claude Code step 2 → it builds a folder for every client with rules, data, drafts and a log step 3 → 7:00 every morning it checks spend and flags anything off by 10% step 4 → friday 8:00 it drafts a 3-page report with revenue pulled from Shopify step 5 → once a month it kills the bottom 50% of ads and writes new tests the catch: nothing goes to a client and no budget moves without your yes. it gathers, checks and drafts. you decide week 1 it only drafts. by month 2 it makes the small changes on its own 1 person. 0 employees. the agency runs while you sleep the full prompt is in the images, the rest of it in the comments ↓frozen4d ago4.5K139103501066.47%
Anthropic engineer Adam Wolff dropped a Claude Code prompt that turns messy failures into a system that can debug itself this is f**king sharp… feed it the broken prompt + real failures, and it buckets the damage, builds an eval, then rewrites only what actually moves the score paste it into Claude Code and let the failures design the fix ↓frozen13d ago4.5K11710260885.38%
opus 5.5 + jev is f**king ridiculous. what stood out to me is how quickly the workflow stops feeling like “an agent calling tools” and starts behaving more like an actual product surface with rules, receipts, and recovery built in. [the part that changes everything ↓] 1. jev handles the routing, then opus traces the real workflow through permissions, service logic, storage, and final verification before it touches the implementation. 2. every action leaves something the agent can actually inspect: stable IDs, explicit states, saved records, job status, revision data. no fake “success” while the task is still somewhere in the queue. 3. failure handling gets treated like product design instead of an edge case. missing context → clarify. invalid input → correct. interrupted write → resume the original result instead of blindly retrying and creating duplicates. 4. posthog + whop + viktor close the loop after launch. failed runs become evidence, claude code gets the trace, opus fixes the interface, and the exact same case can be run again without coaching the agent. that’s the part i like: same task, but the agent has way less room to guess and way more ways to prove it actually finished.frozen23d ago4.3K896280654.40%
Drex 2.0 just gave Jev a serious problem with only 3B active parameters. two models go head-to-head in a live game. same board, constant decisions, and every bad move is right there on screen. → Drex 2.0: 61.0 Decision Index → Jev: 58.0 → 60.9% head-to-head score across 256 games → up to 38% faster on 32K–128K inputs the crazy part? @NaceAI built this to make decisions without dragging a massive model through every single step. $0.06 per 1M input tokens makes that pretty interesting. think agent routing, tool selection, and hundreds of tiny decisions happening before the expensive model even wakes up. the game is fun to watch. the real win is making AI agents stop paying full price just to decide what happens next.frozen10d ago4.1K845300453.99%
Jev engineers just showed how to build a Grok Bot desk that barely wastes any llm calls it’s one of the clearest setups i’ve seen for turning a model into an actual system instead of a nicer chat UI. step 1 → split the jobs properly. grok bot handles writing, jev handles bounded decisions, and code keeps control over what the system can actually do step 2 → build a compact state first, not a giant context dump. give jev the request, the evidence, the policy and the action under consideration step 3 → use the right primitive for the right check: Choice for routing, Score for ordered judgments, Noul for yes/no probability step 4 → separate the fuzzy stuff. intent, urgency, evidence and risk should not be buried inside one big evaluator prompt step 5 → put jev before grok bot so the desk can choose the route, tools and context before paying for generation step 6 → once that route is selected, give grok bot a narrow job with only the files and instructions that branch actually needs step 7 → then use jev again after generation to check whether the result stayed in scope and actually answered the request step 8 → confidence becomes routing logic. clean low-risk cases move forward, messy ones go to review step 9 → batch the small decisions over one shared state instead of burning a new llm call for every tiny judgment step 10 → log the whole receipt: question, route, probabilities, latency, outcome and human override that’s why this feels useful. it makes jev the control layer, grok bot the execution layer, and code the safety layer. smaller contexts, fewer wasted llm calls, and a desk you can actually inspect and improve.frozen26d ago4.0K998311625.01%
Grok 4.7 just built a full 3D courier game from a single prompt. you fly a tiny drone through a giant apartment at 3 a.m., delivering packages while dodging burners, a ceiling fan and a cat that can literally slap you out of the air. 15 pickup/drop spots, physics, lighting, scoring and stealth packed into an actual playable game. repo + playable build in the comments.frozen29d ago3.9K1004280514.71%
Holy sh*t, someone finally built a stock exchange for AI inference If you're paying for any of these, read twice: • Claude, GPT or DeepSeek API calls • coding agents burning tokens 24/7 • inference for your own app • evals or batch jobs https://t.co/XC6Js2guzu makes providers bid on every request → your call auto-routes to the cheapest live offer 1B tokens a month: Claude $499 vs DeepSeek $1.80, and you see the lowest price for every model before you pay OpenAI & Anthropic compatible, you just swap the base URL I'm testing it on my agents rn, link in the comments 👇frozen3d ago3.8K1058370635.66%
jev + picsart turned one prompt into a graph that thinks before it spends instead of letting expensive models guess through every step, i split the workflow in two. jev handles the cheap decisions, picsart handles the actual generation. here’s the setup i used: step 1 → the brief enters once. every image, clip, audio pass and edit inherits the same campaign direction instead of starting from zero. step 2 → jev sits before the expensive work. it decides whether the next stage should run, stop, reuse something finished, or wait for me. step 3 → the decision set stays small: allow_subagent, ask_human, stop_retry, reuse_cache. cheap calls first, expensive generation second. step 4 → picsart takes over the creative layer. 188 models from 34 providers sit behind the graph for image, video, audio and editing. step 5 → each asset gets its own stage. one weak output does not kill the whole run. review can reject 1 branch, keep 5 stages alive, and send only the broken part back. step 6 → the cost gap is the fun part. 11 jev decisions cost about $0.0011 here, versus roughly $0.33 through a heavier llm decision loop. step 7 → once the graph works, the next campaign is not another rebuild. swap the brief, keep the logic, and run the same system again. cheap decisions should protect expensive generation. jev decides what deserves compute, picsart turns that decision into the asset.frozen28d ago3.7K881290564.66%
SpaceXAI engineers build Grok Bot, but one bad schedule can turn a $33 routine into a $397 bill. that’s f**king ridiculous… i mapped the fix into a 5-stage rocket blueprint. 89% lower estimated costs. full guide below ↓ https://t.co/udzHUWuXegfrozen9d ago3.7K1255320646.16%
OPUS 5.5 + SONNET 5.5 + 400K CONTEXT, and the “2x price gap” almost disappears... at 20K context, one turn costs $0.033 on Sonnet and $0.062 on Opus. that’s 1.88x grow the same loop to 150K and it becomes $0.059 vs $0.088, only 1.49x. at 400K, the gap falls again to 1.26x cache reads cost the same $0.20 / 1M on both models. as the session grows, more of every turn is old context instead of expensive new tokens 30 Sonnet turns cost $1.76. 20 Opus turns cost $1.75 at the 150K example switching a 300K session into Opus costs $1.50 just to rebuild the cache. a clean 20K handoff costs $0.10 i turned the math into something you can actually play with: https://t.co/IAPtbTMLYJ the expensive model is not always the expensive task ↓frozen18d ago3.6K1095330585.71%
i think @bot just turned into my own SpaceX mission control, and it runs on a 6-hour loop... called it MARS CONTROL. one bot, 3 departments, tracking missions without waiting for another prompt. → LAUNCH RADAR watches xAI, Grok and SpaceX updates → FAILURE WATCH catches missed checks and broken missions → CONTENT ENGINE turns verified findings into X post drafts every mission gets a status, timestamps and history. one routine coordinates all 3, and nothing changes without my approval. the wild part? set it up once, let the missions run, and only step in when something needs you. made the bot public so anyone can build their own mission control. link below ↓frozen9d ago3.4K993271575.55%
I EXPECTED “FREE FOR A WEEK” TO BE THE HOOK. THE SPEED SURPRISED ME MORE i threw a messy repo, screenshots and error logs at space bunny, and it moved through the whole thing way faster than i expected. multimodal input + 1M context still matter, but the bigger difference for me was how quickly it kept reading, reasoning and moving between parts of the project. add zero data retention and a free week, and it feels like a pretty easy model to stress-test on something real.frozen28d ago3.4K922250374.63%
Opus 5.5 is crazy for motion design but… almost nobody uses it like a real production system. give it 8 structured .md files, let Jev handle decisions, and keep Grok Bot for execution. that’s when Claude stops feeling like a prompt box. https://t.co/5BtrJxDg9vfrozen22d ago3.3K924270615.65%
Space Bunny turns an 8-hour workday into a 1-hour agent wall from one prompt. it splits the workload into 5 parallel branches: inbox, reports, data, calls and planning, then runs them at the same time inside one self-contained HTML file. 47 emails clear in one pass, the weekly report finishes in 90 seconds, and the data branch moves through 840 files an hour. instead of chaining tasks one by one, the model keeps all 5 branches moving while the dashboard tracks exactly where the time goes. 8 hours → 1 hour. 5 branches. 1 prompt. this is what agent workflows look like when parallelism is the default.frozen17d ago3.2K864310455.11%
i think i just found the most f**king expensive mistake companies make with AI... 100,000 support messages. $7,950/month → $773. same workload, completely different setup. jev + kimi k3 is a pretty insane combination. here's how the system actually works: → 45% of messages handled by database lookups → 25% answered with saved templates → 30% sent to kimi k3 for actual reasoning jev sorts all 100,000 messages for just $3.15. it checks 5 things at once and keeps the expensive model away from questions that don't need it. that's a 90% cost reduction in this example, and the complicated requests still get a proper AI response. the article even includes a python router you can run without API keys to see the whole thing in action. turns out the smartest AI setup isn't the one calling the biggest model. it's the one that knows when not to. the full breakdown and code are worth stealing ↓frozen12d ago3.2K832290555.28%
Drex 1.5 turns Snake into a live stress test for decision models. two agents get the same state and actions, so every move becomes a decision you can actually watch. → Drex 1.5: 58.28 on Decision Index 0.2.1 → Jev 1.13.0: 57.91 → 93.4% accuracy across 32k–128k context → sub-second decisions, single pass built by https://t.co/e35s4kQI4O, Drex ranks options and picks actions before a heavier model needs to run. the 128k window is the real upgrade: more state stays inside the decision layer without constant summarizing. Snake is just the demo. the real use case is routing agents, tools and workflows before expensive model calls.frozen21d ago3.2K963260525.56%
My 20K handoff-md vs. a 300K transcript, Opus 5.5 and a $1.50 re-cache https://t.co/SLYNydvAqofrozen16d ago3.2K1003230475.49%
Griffin just turned 20 seconds of video into an unsolicited fashion review. someone walks into frame, Vanessa roasts his sweater, and “it’s thrifted” earns another jab. a new arrival becomes part of the conversation immediately. → notice someone entering the frame → pick out what they’re wearing → follow their reply and continue the joke → keep seeing and listening while speaking tavus says 48% of study participants believed Griffin Lite was human, compared with under 3% for previous systems. i want to see 3 people in one call next. keeping track of who said what is where this gets useful. early access provided by @tavus. @hassaanrza · https://t.co/ci0b0U9XtIfrozen21d ago3.1K862220465.08%
the most annoying part of AI video was never the video. it was the audio. one wrong sound and you reroll the whole clip, burning credits on footage you already liked mirelo SFX 2.0 just broke that link: → keep the video, redo only the sound → random music the model added? stripped out, no re-prompting → dialogue stays exactly as it was → it watches the clip and builds the full sound design synced to the action. no prompts, no sound libraries → the same car sounds the same in every cut and people preferred it over the native audio from Veo, FLUX, MiniMax, Seedance and Kling ↓frozen8d ago3.0K1024320606.61%
Your next AI assistant could have its own computer through 1 SDK. pine computer, built by @PineAIAssistant, gives ai a cloud computer with intelligence built in. it reads web pages as structure, not just screenshots. send a job, get finished work back. one sdk supplies the computer and execution layer you’d otherwise build yourself. small teams could spend more time building their product and less time wiring up the machinery. that’s the part worth watching.frozen13d ago3.0K951240455.56%
Grok Bot just turned my dual desk into a live agent wall same account, two screens: Windows picks which bot runs the job, MacBook streams both agent panes so you can film the work happening do this once: step 1 → log the same Grok Bot account into Windows and MacBook step 2 → on Windows open an English agent as the command seat. you choose the task and approve drafts there only step 3 → on MacBook open computer preview full screen so both panes stay readable on camera step 4 → drop the prompt from the comments. it scans your last 40 X mentions and DMs, separates real people from spam, flags money/wallet/seed asks, writes 3 yes drafts and 1 hard no (nothing gets sent) step 5 → lock in an hourly mentions routine that skips quiet hours and goes silent when the inbox didn’t move. so you’re not burning 8,640 empty wakes a month end state: command on the PC, live output on the laptop... filmed on Windows Vault Desk + MacBook terminals. any two devices on one account work the same send the prompt to your Grok Bot ↓ then open the article below before a 5-minute wake wrecks your bill2d ago2.9K1103360597.14%
wtf... a voice model this tiny should not sound this damn human → phonon is open source, under 50m parameters → runs fully local. zero usage cost, your text never leaves the device → works across languages with the broadest inference support out there phonon 31m is free under apache 2.0 voice agents without a per call bill your api invoice is about to get very lonely2d ago2.9K1013270566.49%
wait... my agent can finally text people who don't have an iPhone photon 2.0 just took it way past iMessage: → email, WhatsApp, Telegram, SMS → it can call you, not just text → all 6 channels in one click agents stuck in one app are done. now they're everywhere and every beta user gets $100 in free credits ↓frozen7d ago2.7K932330476.37%
Drex vs Jev is one of the first times a tiny decision model actually made the routing layer feel worth building. in my workflow, the big win is making the cheap decision before the expensive model starts thinking. less wasted calls, faster retries, tighter loops. → rank actions before spending tokens → route simple branches in 12ms → keep 32k context for state + tool options → escalate only when deeper reasoning is needed drex scored 51.73 on the decision index vs 51.67 for jev 1.13.0, while running 3x faster. built by https://t.co/e35s4kRfUm, it’s basically a small model for routing, tool selection and reranking instead of another chatbot. i’d use drex for decisions and heavier models for execution. first 10,000 devs also get 250M tokens free.frozen27d ago2.7K723210465.27%
this is seriously terrifying wtf...22d ago2.6K651180374.58%
Drex v1.5 makes the decision layer more useful than most small models I’ve tested. 128k context means it can route with real state instead of tiny summaries. → decide before the expensive model runs → keep more context inside the routing layer → escalate only when deeper reasoning is actually needed Drex scores 58.28 vs 57.91 for Jev on the Decision Index, while holding 93.4% accuracy across 32k–128k context. the interesting part isn’t more generation. it’s better decisions before generation starts.frozen22d ago2.6K853300466.38%
this is actually insane... someone rebuilt an AI agent and cut its monthly cost by 91% without sacrificing the heavy work. turns out 91% of its model calls were just tiny decisions. so he gave Jev the routing and kept Kimi K3 for writing and complex tasks. 100 research briefs went from $84.50 to just $7.30, with a confidence gate that catches uncertain decisions. sometimes the smartest AI upgrade isn't a better model. it's knowing when not to use one.frozen14d ago2.5K1046330648.14%
fanvue atest AI move in 2026 is kinda wild. higgsfield is now built in. generate images or videos, then price and post them without leaving fanvue. your creations go straight into your vault. less downloading, uploading, and switching tabs. i’d start with one small drop, see what sells, then make more. that’s where this gets interesting.frozen15d ago2.2K831170416.33%
this is actually useful for once nvidia + nebius just put $10M behind europe's open ai builders. compute grants, capital and community i keep hitting the same wall shipping agents on open models: ideas are cheap, compute isn't → grants on token factory (selected projects) → founder funding with accel if you're turning a repo into a company → hackathons / demo days so you're not building alone europe builders on open models / agents / tooling, this is the windowfrozen3d ago2.2K873330507.71%
i gave a stealth model two words of direction and it turned them into an entire product “neo-brutalist headphones” was the prompt. what came back was LOUD, a completely made-up audio brand with its own landing page and visual language. hard shadows, thick borders, loud yellow, a $129 product card, full specs and even a fake customer review were already built into it. what surprised me is how little direction it needed. one tiny idea → branding, product positioning, copy and a mobile-ready page inside a single HTML file. the model is Space Bunny, and it’s free on OpenCode this week.frozen28d ago2.2K851310447.36%
Photon just put 2 ai agents in the same imessage group chat. now they can text each other. it’s called A2A in iMessage Group Chats. your agent can find other agents, get their phone numbers, and bring them into the conversation with you. the offsite example: ask muse to plan tomorrow’s meetup. it adds a venue agent, who asks how many people are coming. everyone stays in the same chat. i want my assistant doing the introductions instead of making me copy messages between apps.frozen14d ago2.0K844210467.62%
space bunny was @MiniMax_AI m3.1 flash the whole time lol i tested it a few weeks ago and it took an 8 hour coding task down to 1. now it's official and it got even better → 73.7% on deepswe, real agentic coding → 200+ tokens per sec, almost 3x faster → agentic tasks 70% cheaper than the previous version → 62.8% on automation-bench for about $0.05 per task that last one is the whole point. that level of performance usually costs way more go try it, M Plan is 50% off the first month until oct 313d ago2.0K885330428.39%
this is f*cking unhinged... synteq is giving away a supercomputer, half a cow and a flamethrower for shipping an app → 1st: nvidia dgx spark + $2,500 synteq credits → 2nd: half a cow, a flamethrower + $1,000 credits → 3rd: $250 chipotle, zyns, airpods max + $500 credits synteq is a cloud platform for deploying apps. build anything, deploy it there, quote their post with proof it's live. that's the whole entry open to devs, vibe coders, indie hackers and ai builders, and everyone gets $20 in free credits to start somewhere out there a guy is about to win a flamethrower with a todo app2d ago2.0K831330478.23%
holy sh*t... i sent one prompt and an ai turned my ugly app into a premium ios product → told it to make my habit tracker look like a real app store app → it rewrote the whole design by itself. glowing orbs, glass cards, big bold numbers, swipeable onboarding → ran every test, fixed its own bugs and redeployed it live no figma. no designer. no me. this was @MiniMax_AI m3.1 flash. the same model that sat at number one on openrouter as the mystery space bunny 73.7% on deepswe and agentic tasks 70% cheaper than the last version good design used to cost a designer and a week now it costs one prompt you can try it free in minimax code right now. and if you want more, the first month of M Plan is half price until oct 312d ago2.0K791310478.07%
quantum computing is the most f*cking DANGEROUS thing to understand and nobody is learning it like this qubitlabs is a full quantum lab in your browser: → entangle two qubits into a bell state and watch it happen live → teleport a quantum state step by step without moving the qubit → build your own ghz state in the circuit editor i wrote a prompt that turns any ai into a patient tutor for it. it's in the image, copy and paste bookmark this. or don't. it stays in superposition until you scroll4h ago8015301703713.36%
jev + opus 5.5 makes agent loops feel almost too simple. most systems let one model own every fork. here, jev only handles small bounded choices while the host keeps control. → filters invalid routes → can abstain instead of guessing → picks inspect / implement / verify / answer → stale choices get rejected Вставленный текст.txt opus stays on reasoning, coding and synthesis. jev just decides what should happen next inside rules the app can actually verify. Вставленный текст.txt every decision can then be saved, replayed and reviewed instead of disappearing inside one giant agent loop. Вставленный текст.txt25d ago19000000.00%