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bigaiguy

Spencer Baggins

Verifiedactive
@bigaiguy

Love AI and Tech and Dawgpreneurship. Light focus on the DEEP STATE - Lets go to therapy together if possible.

RosterAI Agents & Productivity ToolsAI Model News & ReleasesTech Career & Coding Education
23,875 followers · 958 following · Last polled 57m ago
Perf. Score
41.6
Rank #284
Value Score
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set rates to compute
Median views
1.1K
p25 820 · trailing 7d
Followers
24K
+3.7% 7d
ER (impr.)
2.37%
eng ÷ impressions
ER (foll.)
0.20%
eng ÷ followers
Est. CPM
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needs a rate
WoW views
-98.8%
▼ falling

Rates & value · est. from 1.1K median organic views

Quote tweet
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$75
no CPM
rate 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 (1.1K), not the mean (2.0K) — so a single viral post doesn't inflate a creator's reach. The p25 floor (820) is the level 75% of posts clear.

Recent posts · ranked by views

PostPostedViewsLikesRepostsRepliesQuotesBookmarksER
A French engineer who lives quietly in Paris has spent 30 years writing software that the entire internet now runs on without knowing his name. He wrote the code that streams every YouTube video, every Netflix show, every TikTok clip. He wrote the code that runs the virtual servers underneath AWS, Google Cloud, and Microsoft Azure. He calculated more digits of pi than anyone in history. He has no Twitter. He has no marketing. He just keeps shipping. His name is Fabrice Bellard. Here is the story, because almost nobody outside the systems programming world knows what one man has built. Fabrice was born in 1972 in Grenoble, France. He studied at École Polytechnique, the top French engineering school. He never went to Silicon Valley. He never built a startup empire. He just wrote code. In 2000 he started a project called FFmpeg, an open-source multimedia framework for encoding, decoding, and streaming video. He was 28. The project did one thing nobody else had done well. It handled every video and audio format that existed, in one library, on every operating system. He led it himself for years. Today FFmpeg is the invisible engine of the internet. YouTube uses it. Netflix uses it. VLC uses it. Chrome and Firefox use parts of it. Every Android phone, every iPhone, every smart TV, every video editing tool you have ever touched runs FFmpeg somewhere underneath. If you have watched a video on a screen in the last 20 years, Fabrice's code processed it. He was not done. In 2003 he started QEMU, a machine emulator and virtualizer. He wrote it solo until version 0.7.1 in 2005. QEMU lets you run any operating system on any other operating system. It became the foundation of modern virtualization. KVM, the Linux kernel hypervisor, runs on top of QEMU. Every major cloud provider, AWS, Google Cloud, Microsoft Azure, IBM Cloud, runs virtual machines on infrastructure built around it. The Quick Emulator is the most cited piece of cloud infrastructure code on Earth. He kept going. In 2001 he won the International Obfuscated C Code Contest with a small C compiler that grew into TCC, the Tiny C Compiler. TCC can compile and boot a Linux kernel from source in under 15 seconds. In 2004 he calculated the most digits of pi ever computed at the time, using a personal desktop computer and an algorithm he derived himself called Bellard's formula. In 2011 he wrote a complete PC emulator in pure JavaScript that runs Linux in your browser, a project called JSLinux that engineers still cannot believe is real. In 2019 he released QuickJS, a small but complete JavaScript engine that fits where V8 cannot. In 2021 he released NNCP, a neural network based lossless data compressor that immediately took the lead on the Large Text Compression Benchmark. Then he turned his attention to large language models. He built TextSynth Server, a web server with a REST API for running LLMs locally. He released ts_zip and ts_sms, compression utilities that use language models to compress text and short messages at ratios traditional algorithms cannot reach. He released TSAC, a very low bitrate audio compression system. In December 2025 he released Micro QuickJS, a new JavaScript engine for microcontrollers, separate from QuickJS, designed for environments with almost no memory. Fabrice co-founded a telecom company called Amarisoft in 2012, where he serves as CTO. Amarisoft builds 4G and 5G base station software used by carriers and labs around the world. He has been running it for over a decade while continuing to ship personal projects from his own home page at bellard dot org He has no Twitter. He has no Instagram. He gives almost no interviews. His personal website is a flat list of projects with no styling, no fonts, no marketing copy. Just titles and links. A quiet French engineer who never moved to Silicon Valley wrote the code that quietly runs the internet. He is still shipping.
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A teenager in the United States started publishing software at 14 in 1998, built the entire online infrastructure for the Occupy Wall Street movement in 2011, joined Google as a software engineer, quit in 2018, and then spent five years writing a C library that does something the entire industry said was impossible. Then she combined it with llama.cpp and shipped the easiest way on the planet to run a large language model on any computer. Her name is Justine Tunney. Here is the story, because almost nobody outside the low level systems world knows what one engineer has built. Justine was born in 1984. She started writing and publishing software at 14, back when distribution meant uploading binaries to BBS systems and chat networks. She picked up the handle jart, which she still uses on GitHub today. She did the work most teenagers her age were not doing. She read the systems programming literature. She studied compilers. She fell in love with C. In July 2011 she registered the @occupywallst Twitter handle and the occupywallst dot org domain. Within weeks the protest movement that began in Zuccotti Park in New York had become a global phenomenon, and her infrastructure was the digital backbone of the entire thing. She handled the social media, the website, the donations, the coordination. She built the platform that pushed the movement to reach millions. After Occupy she joined Google as a software engineer. She worked on TensorBoard, the visualization tool for TensorFlow, and on site reliability for Google infrastructure. She stayed for years. Then in 2018 she left Google Brain to work on a personal project. The project was called Cosmopolitan Libc. Cosmopolitan does something most C programmers would tell you is mathematically impossible. It lets you compile a C program once and have the resulting binary run natively on Linux, Windows, macOS, FreeBSD, OpenBSD, and NetBSD with no modification. One file. Six operating systems. No virtual machines. No interpreters. No recompilation. The technique she invented is called Actually Portable Executable. The implications are wild. Cosmopolitan binaries violate every assumption about how operating systems load programs. They are at once a Windows PE file, a Linux ELF binary, a macOS Mach-O binary, and a shell script. The same bytes run on every platform. For five years she worked on it mostly alone. She funded the development partly through Mozilla's MIECO program, which sponsored her work on Cosmopolitan 3.0, released on October 31, 2023. A month later she shipped llamafile. llamafile is what happens when you combine Cosmopolitan with llama.cpp. You take any LLM weights file in the standard GGUF format, you wrap it in Justine's binary, and you get a single file that runs on six operating systems without installation. No Python. No CUDA setup. No dependency hell. Just one file that you double click and it works. Mozilla launched it as an official project of their innovation group on November 29, 2023. It went viral immediately. The repository, hosted at github .com/mozilla-ai/llamafile, now has 24,600 stars. The license is Apache 2.0. Justine kept shipping. She added GPU support to Cosmopolitan, a task systems engineers thought would require rewriting the whole thing. She added dlopen support, another thing nobody else had figured out. She wrote whisperfile, a single file version of OpenAI's Whisper speech-to-text model based on the same architecture. Her GitHub profile lists projects most engineers would consider impossible. sectorlisp, a Lisp interpreter that fits in a boot sector. blink, the tiniest x86-64-linux emulator on Earth. bestline, a teletypewriter command session library. redbean, a complete web server inside a single zip file. A teenager who shipped software in 1998 grew up to write the C library that the entire local AI movement now runs on top of. She did most of it alone, and most people scrolling AI Twitter cannot name her.frozen9d ago200K3.6K67185622.6K3.54%
10 BOOKS THAT MAKE YOU DANGEROUS AT AI 1. The Alignment Problem by Brian Christian Learn why teaching a machine what we want is the hardest problem we have. Christian walks you through the real gap between what we tell AI to do and what we actually mean, and the smart, unsettling ways it goes sideways. You start seeing every AI product as a values decision, not just a tech one. 2. Life 3.0 by Max Tegmark Teaches you to think in scenarios instead of headlines. A physicist maps out where machine intelligence could take us, from boring to utopian to grim, without picking a comfortable lie. Read it and you stop reacting to AI news and start asking which future each release is quietly voting for. 3. The Coming Wave by Mustafa Suleyman Gives you the containment lens on the whole industry. A DeepMind co-founder argues the real question isn't how powerful AI gets, but whether we can contain a technology this cheap to copy and spread. In a hype cycle, this is the book that makes you ask who controls the off switch. 4. Co-Intelligence by Ethan Mollick The practical manual for actually working with these tools. Mollick treats AI like a strange new coworker and shows you how to get real leverage from it today, not in some imagined future. The fastest way to go from playing with a chatbot to compounding your own output with one. 5. Human Compatible by Stuart Russell Teaches you how the field's own founder thinks we should build this. Russell wrote the textbook the whole field learned from, then argued the standard approach to AI is fundamentally wrong. His fix, machines that stay uncertain about what humans want, reshapes how you judge any "confident" system. 6. Atlas of AI by Kate Crawford Gives you x-ray vision for the physical cost behind the magic. Crawford follows AI down to the mines, the water, the labor, and the power draw nobody puts in the demo. Read it and you never again mistake "the cloud" for something weightless and clean. 7. The Worlds I See by Fei-Fei Li The human story behind the vision breakthrough that started this era. Li built ImageNet, the dataset that lit the deep learning fuse, and tells it as both immigrant memoir and scientific journey. It rewires how you think about where breakthroughs actually come from. 8. Superintelligence by Nick Bostrom Teaches you to take the far end of the curve seriously. The book that dragged AI risk into serious conversation, laying out how a system smarter than us could get away from us fast. Dense, but it gives you the vocabulary everyone in the debate is still using today. 9. AI Snake Oil by Arvind Narayanan and Sayash Kapoor A working detector for AI hype and fake capability claims. Two Princeton researchers separate what AI genuinely does from what vendors pretend it does, especially in prediction. In a market drowning in "AI-powered" everything, this is the skill that keeps you from getting sold a graph. 10. A Brief History of Intelligence by Max Bennett Teaches you what intelligence even is before you argue about machines having it. Bennett traces the five breakthroughs in the evolution of brains and maps them onto how AI is being built. You finish it understanding the actual roadmap nature already ran, and where the machines are on it.frozen20d ago10K33172103617.54%
A Dutch programmer released a text editor for the Amiga in 1991 and asked one thing from anyone who liked it. Donate to children in Uganda. The editor became one of the most used developer tools in history. It ships on nearly every Linux and Mac machine on Earth. Millions of engineers have it burned into their muscle memory. And for 32 years, he maintained it himself. His name was Bram Moolenaar. Here is the story, because every developer who has ever typed :wq owes something to this man. Bram was born in the Netherlands in 1961. In the late 1980s he bought an Amiga and wanted the vi editor he had used on Unix. There was a clone called Stevie, but it was missing features. So he started improving it. On November 2, 1991, he released Vim. Vi IMitation at first. Later, Vi IMproved. Then he made a strange choice for a software license. He called it charityware. Vim was free, but if you found it useful, he asked you to donate to ICCF Holland, a foundation he supported that helps children in Kibaale, Uganda. He had worked there as a volunteer himself. For decades, the help screen of the most popular programmer's editor in the world pointed people to an orphanage project in East Africa. Vim spread everywhere. It came preinstalled on Linux distributions and macOS. Every server you ever SSH'd into had it. Generations of programmers learned it, cursed it, and then refused to use anything else. It spawned forks, including Neovim, and inspired the modal editing keybindings found in VS Code, JetBrains IDEs, and almost every serious editor today. Bram kept maintaining it through everything. He worked at Google in Zurich for years and still released Vim updates. He wrote Vim9 script, a new scripting language for the editor, when most people would have handed off the project. Vim 9.0 shipped in 2022, 31 years after the first release. On August 3, 2023, Bram passed away at 62. The community took over. The next major release, Vim 9.1, was dedicated to him. The donation request is still in the help files. A programmer who wanted a better editor on his Amiga ended up shaping how millions of people write code. And the whole time, he kept asking them to help kids in Uganda.frozen7d ago9.5K13035102232.11%
Holy shit... a 61-year-old ex-Google engineer just released the context system that 3 frontier labs are quietly using. No blog. No launch. No name attached. Just a repo that fixes the one thing that breaks every AI agent: memory. Here are the 9 tricks from it I've been using for 3 weeks: ↓frozen24d ago5.6K36770611.99%
10 BOOKS THAT WILL MAKE PEOPLE LIKE, TRUST, AND LISTEN TO YOU Bookmark every one. 1. How to Win Friends and Influence People by Dale Carnegie Builds: the ability to make people genuinely like you. Written in 1936 and still outselling almost everything, because the basics never changed. Use someone's name, talk about what they care about, make them feel important, and doors open everywhere. The foundation every other book on this list quietly stands on. 2. Never Split the Difference by Chris Voss Builds: the ability to win any negotiation without a fight. Voss was the FBI's lead hostage negotiator, and he teaches you the exact moves he used when lives were on the line. Mirroring, labeling emotions, the calm voice that defuses anyone. Works on your boss, your landlord, and your kids. 3. Nonviolent Communication by Marshall Rosenberg Builds: the ability to disagree without it turning into war. Rosenberg's method separates what happened from how you feel from what you need, so a fight becomes a conversation. The framework couples, managers, and diplomats use to say hard things without blowing up the relationship. 4. Crucial Conversations by Kerry Patterson and team Builds: the ability to stay calm when the stakes are high. The talks that actually matter, the raise, the breakup, the confrontation, are the ones where people either freeze or explode. This teaches you to keep the conversation safe and honest at the exact moment it wants to go off the rails. 5. The Like Switch by Jack Schafer Builds: the ability to make people trust you fast. Written by an FBI behavior expert whose job was getting strangers to open up. The friendship signals, the small cues that lower someone's guard, the science of why we like who we like. Reads like a manual for instant rapport. 6. Influence by Robert Cialdini Builds: the ability to see persuasion coming, and use it yourself. Cialdini spent years undercover in sales jobs to find the six levers that make people say yes, reciprocity, scarcity, social proof, and the rest. Once you can name them, you stop getting played and start understanding every ad, pitch, and request. 7. How to Talk to Anyone by Leil Lowndes Builds: the ability to never feel stuck in a conversation again. Ninety-two small, specific tricks for small talk, first impressions, and working a room. The tactical opposite of vague advice, it hands you exact things to say and do. The cure for anyone who dreads walking into a party alone. 8. Difficult Conversations by the Harvard Negotiation Project Builds: the ability to raise anything without making it worse. From the team behind Harvard's negotiation research, it breaks down why hard talks go sideways and how to steer them. It teaches you to lead with curiosity instead of accusation, which changes how the other person hears every word. 9. Emotional Intelligence by Daniel Goleman Builds: the ability to read the room and yourself. Goleman made the case that managing emotions matters more than raw IQ for how your life actually goes. Recognizing what you feel, handling it, and sensing it in others is the invisible skill behind everyone who's great with people. This is where the idea started. 10. Give and Take by Adam Grant Builds: the ability to succeed by helping, not stepping on people. A Wharton psychologist shows that the most successful people are often generous ones, as long as they're generous the smart way. It reframes networking and career-building as something you win by making others better. Proof that being good with people and being good to them are the same skill. Nobody teaches you people. These do.frozen14d ago4.7K16037121337.05%
Claude quietly opened up 10 income streams that most creators have no idea exist. Each one does $1K to $8K a month. One weekend to set up. Zero inventory, zero ads, zero TikTok dances. Save this before everyone else finds out:frozen5d ago2.3K201321252.66%
Nothing was the same after this https://t.co/I56N66hfjMfrozen18d ago2.1K412120.48%
This is insane. An AI can now find moments in your podcast, cut them into clips, add subtitles, and keep the speaker in frame. It’s called Klap. Here's how this works and how you can go viral using it: https://t.co/rLerK5s5zwfrozen1mo ago2.0K21122062.09%
If your authenticator codes sync through Google or iCloud, someone who gets that one cloud account gets every 2FA code you thought was offline. The codes were supposed to live on the phone. They live wherever that backup lives. You did not get a hardware key. You got a screenshot of a hardware key stored in the same account the key was meant to protect. Your authenticator isn't a second factor. It's a copy of the first one. Here's how to check what's syncing and move the crown jewels off the cloud in 15 minutes ↓frozen15d ago1.8K211110182.82%
is Claude down for y'all? they gotta get their sh*t together https://t.co/5cDz2VY2Q6frozen9d ago1.8K624010.74%
If you use Claude Code or Cursor for any security work, install this repo TODAY It's called reverse-skill and it just crossed 36.4k stars Here's the problem it solves: Your AI coding agent has no clue when to reach for jadx vs apktool vs Frida vs IDA vs BurpSuite. Throw it an APK and it starts guessing commands. Half the time it burns 20 tool calls before landing on the right approach reverse-skill fixes this It's a skill router that takes whatever task you're on (APK reverse, binary, JS encryption, malware analysis, CTF, pentest) and maps it to the right methodology, checks which tools you actually have installed on your machine, and runs a real repeatable workflow The stats: → 44 routing rules → 175 regression test cases (CI runs on Windows + Ubuntu on every push) → 45 skill modules covering APK, iOS, binary, .NET, JS, firmware, malware, LLM security, and more → 42 CTF sub-skills → MCP servers for BurpSuite and Reqable → Full Kali Linux integration Works with Claude Code, Codex, Cursor, Cline, Kiro, OpenCode out of the box The killer feature that separates this from every awesome-list style repo: It has a self-evolving knowledge base. Every time your agent hits a mistake, the fix gets logged and reused so the same mistake never happens on the next run Your agent literally gets smarter at security work every time you use it MIT licensed. Free. Install once and forget If you do ANY reverse engineering, CTF, authorized pentesting, or security research with an AI coding agent, this is a must-add to your setup today Repo: https://t.co/1PNkLGKwiAfrozen16d ago1.7K2510160113.68%
I learned microsoft excel by spending $0 online Here are 6 free courses that will teach you excel better than the paid ones:frozen23d ago1.7K15102182.15%
You can turn Claude Code into a better deep research agent than ChatGPT Deep Research It's free. It's open source. And it's called Hyperresearch The problem with every deep research tool: you get one report, then everything it read gets thrown away. Next question, it starts from zero Hyperresearch keeps EVERYTHING Every source it reads lands in a searchable vault on your computer. Plain markdown files. The next time you research anything, it checks the vault first before searching the web Your research literally compounds. Every session starts smarter than the last Here's what happens when you give it one prompt: 1. Breaks your question into pieces 2. Reads 250+ sources in a single run 3. Writes 3 drafts in parallel 4. 4 AI critics attack the report looking for weak spots 5. A patcher fixes only what the critics found. It's locked so it CAN'T rewrite the whole report 6. Every citation gets checked before it ships. Fake quotes get blocked 7. Final report, fully sourced Some details that blew me away: - 5 reprints of the same press release count as ONE source, not five - Retracted papers get flagged and blocked - Paywalled paper? It finds a legal free copy and reads the full thing - Run crashes halfway? It resumes from the exact step it died on Works in Claude Code AND Codex. 3,600+ stars on GitHub The creator says it tops the DeepResearch-Bench leaderboard in internal testing. Third party validation is still pending, but the architecture speaks for itself Setup takes 2 commands: pip install hyperresearch hyperresearch install Then type /hyperresearch followed by any question Fair warning: a full run takes 1.5 to 2.5 hours. Quick questions take 30 to 40 minutes Do yourself a favor and run it on the biggest question you have right now. Go make a coffee. Come back to a report better than anything you'd pay for https://t.co/07MvPnO7rFfrozen10d ago1.7K251121133.12%
GOODBYE EXCEL forever. Grok just made data analysis stupid simple. Upload a messy spreadsheet, ask in plain English, and get charts, trends, and summaries back in seconds. Here are 15 prompts to go from beginner to data-analyst-level in every dataset: (Save this)frozen26d ago1.6K16630283.23%
GOOGLE HAS A COURSE ON USING AI TO FIND A JOB. Here's a 7-prompt interview workout to try before your next interview:frozen17d ago1.6K17920112.42%
10 WEBSITES EVERY INTERNET USER SHOULD CHECK TONIGHT. Bookmark all of them. Most people don't know half of these exist. 1. https://t.co/zwR28T6wZa Shows every data breach your email is in and what got leaked. 2. https://t.co/3c9sMcOYLH Shows every social profile, photo, and login tied to an email address. 3. https://t.co/MOsvtupjHn Free disposable email for any signup you don't trust. 4. https://t.co/t6W6t9kzvQ Burner inbox that self-destructs in 10 minutes. 5. https://t.co/lHWq4ZeJXH A directory of direct links to delete your account from any major service. 6. https://t.co/vpVXkaS6Uc Check if your face was used to train AI image models without consent. 7. https://t.co/cC7q3S3Uui Tells you if your VPN is actually hiding your real location or leaking it. 8. https://t.co/1Q31VRhSQ6 Shows how trackable your browser fingerprint is, even in incognito mode. 9. https://t.co/TVtBWcv6Sw Tells you which programs on your PC are useless bloatware or spyware. 10. https://t.co/yvtYh3ade9 Drop any file or link. It scans against 70+ antivirus engines instantly. The internet is hostile by default. These websites are your free defense.frozen6d ago1.6K211120283.96%
The most useful Claude Code skill I've seen this month has nothing to do with code quality. It fixes the way the agent talks to you. It's called i-have-adhd, and it has 50,000 stars on GitHub. Most coding agents bury the answer. You ask how to fix a failing auth test, and you get a paragraph about middleware, a detour into your dependency versions, and a "Let me know if you want to dig deeper!" The fix is in there somewhere. You have to dig for it every single time. This skill gives the agent 10 rules. A few of them: - Lead with the next action. - Number multi-step tasks. - End with one concrete next step. - Specific time estimates (minutes, not "a bit"). - No preamble. No recap. No closers. The before/after example in the README says it all. The "after" version is one command, three numbered steps, and what to paste back if a test fails. This matters more than it looks: When you run agents all day, reading their output becomes the bottleneck. Every sentence of filler costs you twice: once in tokens and once in your own focus. You can't fix this one prompt at a time. The rule has to live in a file the agent reads every session. It works with Claude Code, Codex, Cursor, Gemini CLI, and OpenCode. MIT license. Repo link below: https://t.co/RWLXIQpw7Ofrozen12d ago1.5K23118063.16%
BIG NEWS: Anthropic just dropped a terrifying report on how criminals are weaponizing AI. And the biggest takeaway is insane: AI isn’t just helping hackers anymore. It’s starting to run the attacks. Here’s what they found: https://t.co/KPw10Y83bKfrozen27d ago1.5K19104132.47%
This man changed the world, and you've probably used his invention 10 times this week without knowing his name. He invented the QR code. You scan it to pay for coffee, to open a restaurant menu, to get into a concert, to join Wi-Fi. He built it to track car parts in Toyota factories. He worked out its key design while playing Go on his lunch break. His company could have charged the whole world for the QR code. They made it free. His name is Masahiro Hara. Here is the story, because almost nobody outside Japanese manufacturing knows what one man designed. Masahiro was born in 1957 in Tokyo. He studied engineering at Hosei University. He joined Denso, a parts maker in the Toyota group, and spent his early career building barcode scanners. He never moved to Silicon Valley. He never started a company. He just worked on scanners. In 1992 the factories came to him with a problem. A barcode could hold only about 20 characters. One box of parts could carry up to 10 barcodes, each scanned separately. Workers were scanning around 1,000 barcodes a day. Barcodes couldn't store Japanese kanji at all. So Hara set out to build a new kind of code. His team had two people. The hard part was speed. A scanner had to find the code instantly, from any angle, even on a dirty, crumpled cardboard box. Hara decided to put markers in three corners so the scanner would always know where the code was and which way it was turned. The markers had to look like nothing else in the world. So the team took leaflets, magazines and cardboard boxes, reduced every printed pattern to black and white, and measured them. They were looking for the ratio of black to white that shows up least often in print. The answer was 1:1:3:1:1. That's why every QR code on Earth has those three nested squares in its corners. He was not done. The Go board solved the next problem. In Go you can read a position even when a stone is slightly out of place, because the stones around it tell you where it belongs. Hara used the same idea so the code could be read roughly first and then fine-tuned. A smudged or damaged QR code would still scan. After a year and a half, in 1994, they had it. The new code held about 7,000 digits instead of 20. It stored kanji. It scanned more than 10 times faster than other codes. Even with part of it torn off, it could still be read. They named it Quick Response. The QR code. Then came the decision that changed everything. Denso Wave owned the patent. They could have charged every company, every app and every printer on Earth. They didn't. They made the QR code free for anyone to use. The reason was simple. Barcodes were already free, and nobody would switch to something that cost money. He wanted everybody to be able to use it. It left the factory. In 2000 the QR code became an international ISO standard. In 2002 Japanese phones started shipping with QR readers built in. In the 2010s Alipay and WeChat Pay made it the way hundreds of millions of people in China pay for everything, from street food to rent. India built a national payment system that runs on QR codes at every tea stall. Then in 2020 the whole world used QR codes to check into restaurants, read menus and show vaccine records. A code built to track car parts became the way the world pays, orders and checks in. He has said he never imagined people would one day scan his codes on the street. He never became a household name. He never started a startup. His company never charged a patent fee. When people ask him why the QR code spread everywhere, he doesn't talk about himself. He says it's because it was free. This man changed the world with a code he built for a car parts factory. Most people will scan it today without knowing his name.frozen13d ago1.4K19135253.07%
10 PODCASTS THAT MAKE YOU DANGEROUS AT AI 1. Dwarkesh Podcast (Dwarkesh Patel) The long-form interviews that get researchers to actually explain their thinking. Dwarkesh runs multi-hour conversations with researchers and lab leads, pushing past the usual talking points into how they reason about scaling, alignment, and where the field is headed. 2. Latent Space Podcast (swyx and Alessio Fanelli) The builder's view of AI, not the philosopher's. This covers the engineering and product decisions behind real AI tools, with the people who actually built them. Practical over speculative, most episodes. 3. No Priors (Sarah Guo and Elad Gil) The venture perspective on which AI bets are actually working. Two investors interview founders and researchers about where the money, the talent, and the real technical progress are actually moving. Useful for separating genuine momentum from funding-round noise. 4. The TWIML AI Podcast (Sam Charrington) One of the longest-running technical AI interview shows, still relevant. Charrington interviews researchers and practitioners on the technical details of their work, often going deeper into methodology than most tech podcasts bother to. 5. Practical AI (Changelog) The show built for people implementing AI, not just discussing it. Focused on real-world applications and engineering trade-offs rather than research news, this is closer to a working session than a news roundup. 6. Hard Fork (New York Times) The mainstream-friendly show that still gets the technical details right. Kevin Roose and Casey Newton cover AI news for a general audience without dumbing it down into nonsense, which is rarer than it sounds. 7. Eye on AI (Craig Smith) A steady read on where AI is actually headed, minus the hype cycle. Smith interviews researchers, founders, and policy people with a journalist's skepticism rather than a fan's enthusiasm. Good for tracking the field without getting pulled into whatever's trending that week. 8. Machine Learning Street Talk The most technically unfiltered debate show in AI podcasting. A panel format that gets deep into disagreements between researchers on what's actually happening inside these models, with far less hedging than most interview shows. Not for beginners, but excellent once the basics click. 9. This Day in AI Podcast The rapid-fire weekly news show for people who don't have time to read every release. A weekly rundown of what shipped, what broke, and what's actually worth paying attention to, delivered fast. Good as a Monday catch-up rather than a deep dive. 10. The Cognitive Revolution (Nathan Labenz) The show that treats every episode like a technical due-diligence session. Labenz interviews researchers and founders with an engineer's skepticism, pushing on claims until they either hold up or don't. One of the better sources for understanding what a new capability can and can't actually do.frozen19d ago1.4K151060102.86%
holy crap… Pocket FM's memory system hits the same 86.7% accuracy as Claude Code's memory harness on Opus 5.x class models. for about 21x less per run. a storytelling company just built one of the most cost-efficient long-context memory setups I've seen.frozen7d ago1.4K300050.58%
I turned ChatGPT into my personal assistant. And now I only work for 60 minutes a day. Let me show you the 10 best prompts I use for productivity:frozen22d ago1.3K1491192.55%
I found 15 AI models that do ONE thing better than models 100x their size: 1. Qwen2.5-Coder-7B → Coding 2. DeepSeek-R1-Distill-Qwen-7B → Reasoning 3. Phi-4-mini → Math + reasoning 4. SmolVLM2 → Image/video understanding 5. Moondream → Computer vision 6. Kokoro-82M → Text-to-speech 7. Whisper small → Speech recognition 8. BGE-small → Embeddings/search 9. Florence-2 → Vision tasks 10. PaddleOCR-VL → Documents/OCR 11. Granite-3.3-2B → Enterprise tasks 12. Gemma 3 4B → General local AI 13. Qwen2.5-VL-3B → Vision + documents 14. Ministral 3B → On-device AI 15. ModernBERT → Classification + retrieval That means you can often get: • Faster responses • Lower memory usage • Offline inference • Better privacy • Dramatically lower costs Bigger ≠ automatically better. Pick the model built for the job.frozen21d ago1.3K19930103.11%
A developer just killed the real estate walkthrough industry by scanned an entire house with his phone. Uploaded it. Now anyone on Earth can walk through it in a browser tab. No app. No VR. No agent. No appointment. Click → you’re inside. Every room. Every angle. Every shadow. Photoreal. The economics are brutal for the old model: → Agent fee on a $500k home: $15,000 → Cost to produce this scan: roughly $200 → Time to "tour" 50 houses: one evening → File size: smaller than a TikTok clip The science is wild too: It runs on 3D Gaussian Splatting instead of polygons. Millions of tiny glowing splats of color and depth reconstruct the scene from your photos, and it loads photoreal on a phone. Freelancers are already charging $300 to $800 per scan for realtors, Airbnbs, venues, and dealerships. One person + one phone + one weekend = a business. Open source. Built on PlayCanvas. Free GitHub: https://t.co/B4eW6rFRKcfrozen28d ago1.3K1580092.55%
10 BOOKS THAT MAKE YOU DANGEROUS AT BUILDING A STARTUP 1. The Mom Test by Rob Fitzpatrick Teaches you how to ask questions that can't be answered with a polite lie. Fitzpatrick shows why customer interviews fail and how to get real signal about your idea instead of compliments. You stop asking "would you buy this?" and start finding out what people already pay for. 2. Zero to One by Peter Thiel Gives you a filter for ideas that are actually new. Thiel argues the goal is building something that doesn't exist yet, not copying what works. After reading it, you can't look at a crowded market without asking what the secret is that nobody else has found. 3. The Lean Startup by Eric Ries Teaches you to treat every product as an experiment. Ries gives you the build, measure, learn loop and the idea of a minimum viable product. It's the fastest way to stop spending six months building something nobody asked for. 4. The Hard Thing About Hard Things by Ben Horowitz The book about what nobody tells you once the company is actually in trouble. Horowitz writes about layoffs, bad hires, and near-death moments from his own career with no motivational polish. You come away knowing there's no playbook for the worst days, only decisions. 5. Crossing the Chasm by Geoffrey Moore Shows you why early adopters and the mainstream are two different markets. Moore explains the gap where promising tech products stall and die, and how to pick one narrow beachhead to cross it. Essential if your product has fans but no growth. 6. Hooked by Nir Eyal Teaches you how habit-forming products are built. Eyal breaks down the trigger, action, reward, and investment loop behind apps people can't put down. Read it once and you'll see the loop in every product on your phone. 7. The Innovator's Dilemma by Clayton Christensen Explains why great companies fail by doing everything right. Christensen shows how well-run incumbents ignore small, cheap competitors until it's too late. It's the best lens for understanding why a scrappy newcomer can beat a giant. 8. Obviously Awesome by April Dunford Teaches positioning, the thing most founders get wrong. Dunford gives you a step-by-step method for deciding what your product is, who it's for, and why it's different. A good product with weak positioning looks mediocre, and this book fixes that. 9. High Output Management by Andy Grove The operating manual from the man who ran Intel. Grove treats management as a system with inputs, outputs, and leverage, not a vibe. It's short, dense, and still one of the best books on how to run a team. 10. Shoe Dog by Phil Knight Shows you what building a company actually feels like. The Nike founder tells the story of cash crunches, lawsuits, and constant near-collapse in the early years. You finish it knowing that most great companies were never smooth.3d ago1.3K171131123.52%
Holy sh*t... Pocket FM trained an AI writing partner on 100M+ hours of listener behavior and handed it to writers. Sherpa reads episode-level retention, coin spend, and the exact point people drop off. Then it takes one premise and builds you a full season. Every AI writing tool I have tried guessed at what holds attention. This one measured it. The company is at $500M ARR and raising $120M at around $2B.frozen19d ago1.1K711051.22%
Holy crap. OpenAI has 120+ YC founders working inside it. @thsottiaux says it feels like a giant startup. Unlimited tokens included. He also thinks Google is holding itself back. Most honest lab-vs-lab take I've heard all year...frozen6d ago1.1K210000.28%
The fingers turning that nut deserve a close-up. Wild how much control it hasfrozen23d ago1.0K401010.60%
Bro thinks he's him https://t.co/C36JJfxdycfrozen29d ago968211000.41%
New hackfrozen18d ago916501020.87%
If you read books and forget everything, this is for you. NotebookLM can turn any book into action plans, memory notes, and usable insights. Here are 12 prompts: https://t.co/MZhBQ544Pefrozen1mo ago9131021051.97%
In early 2018 a former Kaggle champion showed that the way people trained language models was backwards. Within months, OpenAI and Google built their breakthrough models on the same idea. His name is Jeremy Howard. Here is the story, because he is a teacher who accidentally changed the research field. Jeremy grew up in Australia. After studying philosophy he worked as a management consultant, then co-founded Fastmail, the email service. He moved into data science and became the top-ranked competitor on Kaggle, then its president and chief scientist. He founded Enlitic to apply deep learning to medical imaging. In 2016 he and Rachel Thomas started Fasr AI. Its premise was that you do not need a PhD or a datacenter to do serious deep learning. Their free course, Practical Deep Learning for Coders, taught people to build real models from the first lesson. Thousands of students used it to change careers. He was not done. In January 2018 he and Sebastian Ruder published ULMFiT. The idea was simple. Pre-train a language model on a large body of general text, then fine-tune it on your specific task. In computer vision, transfer learning was standard. Nobody had made it work reliably for text. ULMFiT reduced error by 18 to 24 percent on most of the datasets it was tested on. With only 100 labeled examples it matched the performance of models trained from scratch on 100 times more data. OpenAI published GPT-1 in June 2018. Google released BERT in October. Both built on the same recipe: pre-train on a vast amount of text, then fine-tune. That recipe became the foundation of the entire field. In 2023 he co-founded Answer AI with Eric Ries. In 2024 the lab worked with Tim Dettmers to make it possible to train very large models on consumer GPUs. He also proposed llms.txt, a file that helps websites speak to language models. His courses are free. His library is open source. The most influential idea in modern language AI came from someone whose main job was teaching people how to use it.2d ago9041893033.65%
Your Slack update shouldn't need a prompt collection. I just posted 10 ChatGPT productivity prompts. mio drafts the update before you ask, 0 prompts. Awkward timing for my thread. Beta open, first 100 companies. Save this. https://t.co/VKYhViLVMcfrozen21d ago887601010.90%
This is the first AI company I’ve seen built solely because humans are dumbfrozen9d ago882100000.11%
DHH open-sourced his entire Linux setup as one install script. Here is the pattern it kills. Developer decides to try Linux. Spends two weeks on dotfiles. Fights Wayland. Gives up. Goes back to macOS. Tells everyone Linux isn't ready. Omarchy removes the entire decision surface. Install Arch, run one command, land in a fully configured tiling desktop that already looks and behaves like someone thought about it. → 32.2k stars, 3.3k forks, 6,000+ commits → Hyprland compositor with animations, keybindings, per-monitor config → curated stack: Alacritty, Neovim, browser, containers, password manager → one theme applied consistently across terminal, editor, GTK and QT → a real migration system, so an install from six months ago upgrades cleanly That last one is why this is more than a dotfiles repo. Most of them rot. This one ships `omarchy update`. It's opinionated on purpose. Neovim over VS Code, Hyprland over GNOME. Swap those out and you've defeated the point. Maintained under the Basecamp org. 100% Open Source.frozen29d ago878733041.94%
I think you got hacked @juliafedorin ? https://t.co/s0BofBSPy3frozen23d ago846333001.06%
This repo shrinks your AI skills by turning them into images. (100% open-source, and it's part of a 103k-star project) Every skill you install is prompt text. Your agent reloads that text on every single call, so a long skill costs you tokens every turn. Caveman's Pixel mode takes a different route. `caveman convert` renders the skill body into PNG pages in place, and the model reads those pages as an image instead of text. On the caveman skill itself: ↳ 1,069 estimated tokens down to 415 ↳ A 61% cut The safeguards are what make it usable: → It only converts when the image pages beat the text → Any failure leaves the skill byte-identical and names the gate that said no → `caveman convert --dry-run` shows the token math for every installed skill with no writes → `caveman convert --revert` restores the original byte for byte from SKILL.orig.md Run it per agent: caveman convert --agent claude Note that these are estimated tokens from the maintainer, so check your own numbers with the dry run first. The skill itself is MIT. The engine runtime is BSL-1.1 source-available. GitHub repo: https://t.co/KLj5l0H6kA16h ago8201190002.44%
Doesn’t freeze when the wheel doesn’t seat. Watches the prompt understands the end state and keeps working until it gets there.frozen28d ago803300010.50%
Holy crap. $140M ARR in 90 days, and AP learned invoice approval from a screen recording - even on a vendor portal with no API. The so-what is in the video. https://t.co/aZtNAcrU3Zfrozen16d ago798400020.75%
Chat GPT, Claude or Grok are all useless without your data. But that’s a problem. This is the solution.frozen20d ago772100020.39%
Most of the reason people are poor is because it’s too hard to move money. The next giant fintech won’t come from SoMa. I think Fin just proved thatfrozen23d ago769201010.52%
with 500k+ claims, BackOps probably heard every version of “it was supposed to be there Tuesday”frozen22d ago742100000.13%
new AI benchmark: can my mom make something useful without knowing what a model is13h ago551411111.45%
Bad acting might finally be optional. An intensity slider that lets you keep the performance but fix the emotion is a real shift for anyone who has ever had to reshoot a take just for the face.6h ago369200021.08%
Holy crap. $140M ARR in 90 days, and AP learned invoice approval from a screen recording - even on a vendor portal with no API. The so-what is in the video. https://t.co/AsOAWPSPxe16d ago268200011.12%