𝗞-𝗺𝗲𝗮𝗻𝘀 𝗶𝘀 𝘀𝗶𝗺𝗽𝗹𝗲. 𝗠𝗮𝗸𝗶𝗻𝗴 𝗶𝘁 𝗳𝗮𝘀𝘁 𝗼𝗻 𝗚𝗣𝗨𝘀 𝗶𝘀𝗻’𝘁.... That’s why we built Flash-KMeans — an IO-aware implementation of exact k-means that rethinks the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves 30x speedup over cuML and 200x speedup over FAISS — with the same exact algorithm, just engineered for today’s hardware. At the million-scale, Flash-KMeans can complete a k-means iteration in milliseconds. A classic algorithm — redesigned for modern GPUs. Paper: Code:show more

forget the $699 AI pins. this $8 chip just... shattered the barrier for local AI hardware. a developer just forced a 28.9 million-parameter LLM onto a standard ESP32-S3 microcontroller. it costs roughly 8 dollars, runs completely offline, and draws the power of a single LED. conventional wisdom said a model of this size simply would not fit. the chip only has 512 KB of fast SRAM and 16 MB of flash. the breakthrough is architectural. the developer moved the bulk of the embedding table into flash memory and memory-mapped it. the chip only needs to pull about 450 bytes per token, keeping the active working memory inside the fast SRAM. this means you can now embed a capable language model into a physical node for the price of two coffees. and we are already seeing the beginnings of this custom physical hardware. in the video, a creator built a minimalist voice-controlled universal remote using an ESP32. it captures voice and remotely controls the computer over bluetooth LE. he simply says "open chrome and open 20 new tabs", and the custom hardware executes it instantly. we have spent years watching model sizes explode upward. but the true frontier is the opposite direction. when an eight-dollar chip can power offline intelligence and custom physical interfaces, AI becomes local infrastructure rather than a cloud service.show more

Big moment for Postgres! Search has always been Postgres'... weak spot, and everyone just accepted it. If you needed a real relevance-ranked keyword search, the default answer was to spin up Elasticsearch or add Algolia and deal with the data sync headaches forever. The problem isn't that Postgres can't do text search. It can. But the built-in `ts_rank` function uses a basic term frequency algorithm that doesn't come close to what modern search engines deliver. So teams end up: - Running a separate Elasticsearch cluster just for search - Building sync pipelines that inevitably drift out of consistency - Paying for managed search services that charge per query - Accepting mediocre search relevance because "good enough" ships faster But this is actually a solvable problem. You can realistically bring industry-standard search ranking directly into Postgres, which eliminates the need for external infra entirely. This exact solution is now available with the newly open-sourced pg_textsearch by Tiger Data - Creators of TimescaleDB, a Postgres extension that brings true BM25 relevance ranking into the database. BM25 is the algorithm behind Elasticsearch, Lucene, and most modern search engines. Now it runs natively in Postgres. Here's what pg_textsearch enables: - True BM25 ranking with configurable parameters (the same algorithm powering production search systems) - Simple SQL syntax: `ORDER BY content 'search terms'` - Works with Postgres text search configurations for multiple languages - Pairs naturally with pgvector for hybrid keyword + semantic search That last point matters a lot for RAG apps. The video below shows this in action, and I worked with the team to put this together. You can now do hybrid retrieval (combining keyword matching with vector similarity) in a single database, without stitching together multiple systems. The syntax is clean enough that you can add relevance-ranked search to existing queries in minutes. pg_textsearch is fully open-source under the PostgreSQL license. You can find a link to their GitHub repo in the next tweet.show more

❗️🌊🇺🇲 - Tropical Storm Helene has caused catastrophic conditions... in western North Carolina, particularly affecting communities around Asheville and Boone. The National Weather Service issued an urgent warning about a potential dam failure at Lake Lure, urging residents downstream to seek higher ground. The storm has led to unprecedented flash flood emergencies in Asheville, with the state’s Department of Transportation advising that all roads in western NC are closed. Governor Roy Cooper described the storm as possibly the worst in modern history for the region, with over 29 inches of rain reported in parts of Yancey County and significant road washouts. The storm has resulted in at least 17 fatalities, including two in North Carolina, and left nearly 1 million people without power in Florida and more than 3 million across the Southeast. Helene's extensive and rapid intensification has made it one of the most damaging storms on record for the region.show more

Introducing ASAL: Automating the Search for Artificial Life with... Foundation Models Artificial Life (ALife) research holds key insights that can transform and accelerate progress in AI. By speeding up ALife discovery with AI, we accelerate our understanding of emergence, evolution, and intelligence–core principles that can inspire the next generation of AI systems! We proudly collaborated with MIT, OpenAI, Swiss AI Lab IDSIA, and Ken Stanley on this exciting project. Full Paper (Website): Full Paper (arxiv): Code: In this work, we propose a new algorithm called Automated Search for Artificial Life (“ASAL”) to automate the discovery of artificial life using vision-language foundation models. Instead of tediously hand-designing every tiny rule of an Alife simulation, simply describe the space of simulations to search over, and ASAL will automatically discover the most interesting and open-ended artificial lifeforms! Because of the generality of foundation models, ASAL can discover new lifeforms across a diverse range of seminal ALife simulations, including Boids, Particle Life, Game of Life, Lenia, and Neural Cellular Automata. ASAL even discovered novel cellular automata rules that are more open-ended and expressive than the original Conway’s Game of Life. We believe this new paradigm may reignite ALife research by overcoming the bottleneck of manually designed simulations, thus advancing beyond the limits of human ingenuity.show more

The term "continual learning" has become overloaded if you... see it as an ML problem. One classic thread is about memorization: regularization-based continual learning methods, such as EWC, MAS, and SI, estimate which parameters mattered for previous tasks and resist changing them too much. One modern thread is about adaptation: test-time training and inference-time learning methods, such as TTT, adapt part of the model on the incoming test stream before making predictions. These are sometimes discussed as separate threads. But in modern scalable architectures, I think they are better seen as complementary constraints: a model that learns quickly at test time also benefits from a mechanism for deciding what not to forget. In our #ECCV2026 paper, we study this in large-scale 4D reconstruction: how to build fast spatial memory that can adapt over long observation streams while reducing collapse and forgetting. Instead of using fully plastic test-time updates, we stabilize fast-weight adaptation with an elastic prior that balances adaptation and memory. Key ideas: - Elastic Test-Time Training: Fisher-weighted consolidation for fast-weight updates - EMA anchor weights that provide a moving reference for stability - Chunk-by-chunk inference for long 3D/4D observation streams We show that this scales across large 3D/4D pretraining settings, including both LRM-style and LVSM-style models, and improves reconstruction across benchmarks including Stereo4D, NVIDIA, and DL3DV-140. We release model checkpoints across different design choices: resolution, post-training curriculum, and whether the model uses an explicit 4DGS intermediate representation. - Homepage: - Paper: - Code: - Models: This work is co-led with Xueyang Yu, contributed by Haoyu Zhen Yuncong Yang, and advised by Michigan SLED Lab Chuang Gan.show more

Say hello to Boojum 👋: zkSync Era’s new high-performance... proof system for radical decentralization. Boojum is an upgrade that will transition zkSync Era to a STARK-powered proof system, providing world-class performance on consumer-grade hardware. 💡 Learn more: TL;DR 👇 Boojum is the name of our Rust-based cryptographic library, which we use to implement the upgraded version of the ZK circuits for zkSync Era and the ZK Stack. The name Boojum was inspired by Lewis Carroll's poem "The Hunting of the Snark," where the Boojum represents the most fearsome kind of Snark. We intentionally designed zkSync Era in a way that cryptographic upgrades can be made without a regenesis, meaning that the Boojum upgrade won’t cause any user disruptions. Why Boojum❓ From day one, zkSync’s mission is to advance personal freedom for all — making digital self-ownership universally accessible by building a blockchain network that is trustless, secure, permissionless, affordable, easy to use, resilient and limitlessly scalable. Boojum plays an important role in advancing this mission by delivering: 1. World-class performance zkSync Era’s current SNARK-based proof system is effective today, but it won’t scale to the volume that we envision for hyperchains. zkSync Era’s sequencer can already process over 100 TPS; Boojum orders of magnitude improvements to performance complements this well. 2. Reduced hardware requirements for decentralization Our long-term goal is to enable user-powered, decentralized proof generation. Boojum represents a breakthrough in this direction — with the prover running on consumer-grade GPUs requiring only 16 GB GPU RAM. Boojum’s Journey to Mainnet 🚴🏽‍♀️ Boojum is now live on Mainnet, generating and verifying ‘shadow proofs’ today with real production data so that we can carefully test the system ahead of fully migrating. Today, we’re also open-sourcing the repo; if you’d like to take a look, you can find it here 👇 This is the first of a series of posts on Boojum. We will provide updates on our progress, including more details on implementation, security, and performance. Watch here for more, anon ∎show more

$25K+ profit daily from 1 wallet, with OpenClaw. I... have the exact step-by-step guide, giving it free for 24 hours. To get it: 1. Comment "OpenClaw" 2. Like and Retweet. 3. Follow me Himanshu Kumar ( So, i can send you DM) I ran a simple script last night with Claude Code. Pull on-chain data from Polymarket, sort by win rate on 15 minute BTC markets. 20 minutes later, 100s of wallets showed up. Most were losing money or barely breaking even. Then I spotted 1 address. 200+ trades daily, every single week profitable, timing so precise it looked robotic. Because it is. I fed the wallet address back into Claude Code. Asked it to reverse engineer the strategy. 20 mins later the full breakdown appeared on my screen. Here is how it works: Bot monitors Binance and Bybit every 100ms. Waiting for BTC volatility compression to drop below 0.08%. When it hits that level, it buys both Up and Down contracts at 25 to 35 cents each. Classic straddle play. 1 contract loses, the other rockets to a dollar. Entry at 30 cents means 3x to 4x return every time. Repeats dozens of times per day. Result: $13K to $25K profit daily from 1 wallet. No human intuition, no insider tips. Just an algorithm exploiting a gap in market mechanics. I searched to see if anyone else found this wallet. Turns out yes. There is a Telegram bot that auto-copies trades from wallets like this. I connected it to the same address. Every entry matched what my terminal showed. You can now copy-trade an algorithm in real time. That capability did not exist 12 months ago. Comment "OpenClaw" and I will send you everything. Must Follow me Himanshu Kumar to get the DM.show more

🚨BOMBSHELL: Drone CAUGHT ON VIDEO Above Charlie Kirk at... the MOMENT Of The SHOT –Mystery DRONE HOVERS Exactly Where the Shot Originated According To All The EVIDENCE 🪰🎯 Just a Reminder: We Believe Charlie Kirk Was Assassinated by a Sniper Drone from His Right Side – And the Video Evidence Speaks for Itself. Based on multiple lines of evidence, including sound analysis using echolocation to trace the origin, less credible but notable crack-boom analysis, a projectile visibly captured on camera, and the pressure wave (the visible pressure disturbance created as the bullet slows from supersonic down to subsonic) that we originally mistook for a muzzle flash reflection in one of the windows of the BA building, but closer examination reveals it as the aerodynamic signature of a high-velocity round. We conclude the shot came from a drone positioned over Kirk's right side. In this video from the event shown below, we can see an unidentified drone hovering precisely on Kirk's right flank—the exact location our analysis indicates as the source. This aligns with advanced military capabilities, such as those detailed in declassified UAP documents from the US Military as well as the capabilities of Israel's number one drone manufacturer "Elbit System" which not-so coincidentally has a facility right there in Utah. If this was a drone strike, who authorized it, and why the cover-up? Tag Candace Owens and Retweet if you're demanding a full investigation, and drop a 🔎 in the comments if you agree the evidence points to foul play.show more

The Sun feels warmer at noon than in the... morning or evening, because it is physically right above you at local noon 🌞 Its heat is not traveling 93 million miles though a vacuum to get to you. This preposterous distance was made up to block you from understanding that it's a local luminary that was put here by God, circuiting above our Earth in perfect circles every day, its path narrowing and widening over the course of the year, which creates SEASONS. This geocentric model of how the Sun interacts with Earth makes complete sense. Unlike the heliocentric model, which makes zero sense, but everyone believes it anyway since they were taught it when they were too young to ask discerning questions like, "If the Sun and Moon are radically different sizes and distances, how come they've looked the EXACT same size in the sky to us here on Earth, for thousands of years of recorded history?" The deceivers in control of the modern matrix don't want you asking these questions, because it leads to you waking up on where you are, and therefore WHO you are, and why you're actually here inside God's creation.show more

Free NVIDIA GPU with 16 GB VRAM GPU for... Running Local LLMs! If you want to master local LLMs but you're waiting until you can afford a $1,500 GPU, you're honestly not going to make it. The open source AI ecosystem is moving way too fast for you to wait on your budget to catch up. Especially when you can build a bleeding edge inference engine from scratch right now, completely for free. You don't need a heavy local rig to start. Google is literally letting you use an enterprise grade NVIDIA Tesla T4 GPU for $0/hour. At standard cloud computing rates (~$0.20/hr), Google Colab’s 4 hour daily free tier hands you roughly $24 worth of data center tier GPU compute every single month. And most people just waste it. Let’s talk about the hardware you get access to for free. The NVIDIA Tesla T4 is an absolute workhorse: - Architecture: NVIDIA Turing (TU104) - VRAM: 16GB GDDR6 (320 GB/s bandwidth) - Compute: 320 Tensor Cores | 2560 CUDA Cores - Performance: 130 TOPS INT8 | 8.1 TFLOPS FP32 - Power: Sipping energy at a max 70W TDP This is the exact same hardware I used to run DeepMind's Gemma 4 26B A4B QAT MoE at a 250,000 context window without a single Out Of Memory (OOM) crash. If you have a web browser and 10 minutes, you have everything you need. I’ve put together a fully documented, cell by cell Google Colab notebook that teaches you exactly how to do this. Here is what the notebook actually teaches you: - How to provision an Ubuntu Linux environment with CUDA 13.0 and verify your driver stack. - How to pull the source code and compile the latest llama.cpp C++ binaries from scratch, specifically optimizing the build for your exact GPU using the -DCMAKE_CUDA_ARCHITECTURES=native flag. - How to directly download quantized local LLMs (GGUF format) straight from HuggingFace using the CLI. - How to manage 16GB VRAM limits, offload neural network layers to the GPU, and push massive context windows. Compile raw llama.cpp, ollama run a model, or spin up the LM Studio CLI. Pick whatever stack you are comfortable with. just start building. No hardware. No credit card. No excuses. Bookmark this post right now so you don't lose the tutorial. Even if you don't have time to run it today, you are going to want this workflow in your engineering toolkit. The link to the free Colab Notebook is in the comments below. Lemme know if you need more tutorials like this.show more

🚨 Do you understand what Claude just quietly dropped... while everyone was distracted? 1 million tokens. Let me explain what that actually means because the number alone doesn't hit right. > A senior engineer joins a company and spends 3 to 6 months just reading code.. Understanding how things connect. Learning where the bugs hide. Why that one file nobody touches exists. It takes months because a codebase is massive and human memory is small. > Claude just loaded the entire thing in one prompt. 30 seconds. Every file, Every function, Every line. All of it. Sitting in memory like it's been working there for years. And it scored highest among every single frontier model. Not GPT.. Not Gemini, Nobody. > Yesterday Amazon's AI nuked production because it couldn't see the full picture - it made a decision with partial context and deleted everything. Today an AI can hold 1 million tokens of context at once. That's the fix. That's the "before and after" moment for AI coding. > 600 images in one request. Entire PDFs. Full repos. And they dropped it on a Friday on all plans like it was a patch note. The scariest AI updates aren't the ones with press conferences. They're the ones that drop in a tweet at 6pm and change everything by Monday morning.show more

🚨 ANOTHER BRAND NEW VIDEO DIRECT FROM IRAN: The... call to be armed has evolved. This is no longer about small-scale tactics or asking for pistols to target low-level regime forces; it is the deployment of a next-level strategy. A highly sophisticated, organized force inside Iran is preparing for a decisive action to finish the matter. These individuals are sending videos directly to me to break through the silence and project a completely new reality on the ground. This latest footage reveals a group operating east of Tehran near Pardis and Jajrud, confidently displaying their exact GPS location before setting up a fleet of FPV and surveillance drones. Their message is absolute: the era of disorganized vigilantes playing around with guns is over. They are operating with modern tools and calculated precision. On the audio track, a mechanically disguised voice delivers a clear warning directly to the state: "This time you are in the center of our target. We are not waiting for the help of others, and we are waiting for you where you do not expect it." The message ends with the declaration "Long live Iran, Javid Shah" (Long Live the King). While they are maintaining strict operational security by keeping their exact targets and timelines secret, they promise the results will be seen soon. For the millions they represent, peaceful protest against live ammunition has proven impossible. They aren't asking for foreign boots on the ground. They are demonstrating that if Crown Prince Reza Pahlavi has a force at his command, that force is intelligent, advanced, and ready. And let’s be clear: the only viable path to effectively arming and coordinating this movement is through direct alignment with the Prince and his team. They risked their lives to deliver this proof straight from the ground. More people need to amplify their call. Do not let the regime bury their bravery.show more

I just built a Claude Code skill that scores... whether your landing page actually keeps your Meta ad's promise 🤯 Drop in your ad and the page it points to. It reads both, scores the "ad scent" from click to page, and finds the exact line where the page breaks the promise that won the click. All inside Claude Code. Perfect for DTC brands and media buyers who pour everything into the ad and the CPA but never grade the seam in between. If you're scaling spend on a winning ad, the click is landing on a page that opens with something slightly different, the ad promised 50% off and the page shows full price, the ad hooked "for oily skin" and the page is a generic homepage, and nothing looks broken, but the visitor feels it and bounces... That gap has a name in conversion work: message match. And you already paid for the click you're losing. Here's what it does: → Drop in your ad (headline, copy, offer, CTA) and the landing-page URL → It fetches the live page and reads what's actually above the fold → Grades 7 continuity dimensions: promise, offer, angle, CTA, audience, proof, visual → Shows your ad's words next to your page's words, so every gap is right there → Rewrites your hero headline so the page keeps the ad's promise → Renders a dashboard with a Match Score out of 100 No guessing why the click bounced. No blaming the creative for a page problem. No buying more traffic to fix a copy problem. What you get: → A Match Score on every ad-to-page pair before you scale → The ad-side vs page-side quotes, side by side, for every leak → A hero rewrite you can paste straight onto the page → A dashboard you can hand to your team or client I'm giving away the full skill completely for free. Built 100% in Claude Code. No API keys. Want the skill? > Like this post > Comment "MATCH" And I'll send it over (must be following so I can DM)show more

I went a little overboard with Codex last week... and burned through my entire weekly allowance in two days. Luckily, my quota reset today. Otherwise, I’m not sure what I would’ve done. It got me thinking: instead of asking one large model to handle everything from start to finish, why not let a stronger model plan the project and review the work, while a model built for execution handles the day-to-day implementation? So I tried it. The result was better than I expected. I used GPT-5.6 Sol in Codex as the decision-maker, then ran Ling-3.0-flash from Ant Ling inside OpenCode as the execution engine. Together, they built a small 3D farming game. Before writing any code, I had Codex create four documents: SPEC.md defined the product scope and the lines we couldn’t cross. ARCHITECTURE.md laid out the isometric coordinate system, state machine, and module boundaries. TASKS.md broke the project into small jobs Ling could tackle one at a time. ACCEPTANCE.md explained how each step would be tested and what “done” actually meant. Then I gave Ling a very straightforward role: You are the execution model for this project. Read all four documents before you begin. Work only on the task assigned for this round. When you’re done, run typecheck, test, and build. If anything fails, read the error, fix it, and run the checks again. Do not move on to the next task early. Ling handled dependency installation, project structure, strict TypeScript configuration, test setup, and a production build in 6 minutes and 3 seconds. It ran into issues with the Vite test config, a TS6310 error, and a missing jsdom dependency along the way. Instead of stopping at the first error, it kept reading the logs and fixing the problems until all three checks passed. The speed was honestly hard to believe. If you exclude the time spent waiting on tools, it was producing more than 100 tokens per second. That made the whole development loop feel noticeably faster. After this experiment, I’m planning to keep using the same workflow. If the task is small, there’s no reason to call an expensive planning model for every single step. If the task is large, handing the entire project to a Flash model in one prompt isn’t a great idea either. The setup that makes more sense to me is: Use a more capable model such as Codex to explore the project, make architectural decisions, and break the work down. Put the constraints into specs, schemas, types, and tests instead of leaving them buried in chat history. Give Ling-3.0-flash a steady stream of clear, verifiable implementation tasks. Report bugs with structured context and actual error logs, rather than saying, “It still doesn’t work.” Bring Codex back in for architecture reviews, visual checks, and changes that affect multiple parts of the project. The point of this setup isn’t to give AI a big “build the whole project” button. It’s to turn software development into a pipeline with a much more sensible cost structure: Codex figures out the plan, sets the boundaries, and catches problems. Ling-3.0-flash moves quickly, calls tools reliably, and works through well-defined tasks at scale. For agent workflows that involve lots of repetitive edits, production tasks, and tool calls, this may be a more practical answer than simply using the biggest model for everything.show more

I just built a Meta ad policy checker in... Claude Code that catches rejections BEFORE Meta does 🤯 Drop in your ad copy → it pulls Meta's LIVE Advertising Standards, checks every line against the actual policy text, and hands each ad a verdict: Cleared for launch, Fix before launch, or Grounded. All inside Claude Code. Perfect for media buyers and DTC brands who've had ads bounced — or an account restricted — and never got a straight answer why. If you're finding out about policy problems only after the rejection email, resubmitting the same ad and praying, losing days of delivery while the appeal sits in review, and every bounce quietly teaches Meta to trust your account a little less... This runs the review before Meta ever sees the ad: → Drop in your ad copy (one ad or a whole batch) → It reads each ad and figures out which of Meta's policies apply → Scrapes the live policy pages from Meta's Transparency Center → Flags the exact phrase that violates, with Meta's own policy quoted next to it → Rewrites the risky lines so the message survives but the violation doesn't → Renders a dashboard: every ad, every finding, every fix in one place No guessing which word killed the ad. No resubmit-and-pray loops. No stacking rejections on your account history. What you get: → A verdict on every ad before you spend a dollar → The violating phrase + the policy citation, side by side → Rewrites that keep the selling intent → A report you can hand straight to your team or client Built 100% in Claude Code. No API keys, no Meta login. I'm giving away the complete Claude skill file. Want the skill for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)show more

Are you an Insurance Advisor/Mutual Fund Distributor or advise... clients on financial products? To make life easier for financial advisors, we have now integrated the ProtectMeWell API into a Google Sheet This means that you can work on a Google Sheet and generate reports (👇) like these for your prospective customers in real-time. 🚀Would you want to be an early adopter of "ProtectMeWell on Google Sheets"? Here is the deal 1⃣ I am seeking the help of 20 passionate advisors for closed user group testing. On a first come, first serve basis. DM me, if you are interested 2⃣ I will extend access to the Google Sheets for two weeks for ₹999. 100% refundable when you provide me with at least 5 actionable areas of improvement from a usability perspective of Google Sheets. 3⃣ When it is launched for the wider audience (late August 2023) you get to use it for free until 31 March 2024 (subject to 10,000 API calls) PS: To clarify, it is not the testing exercise of API (or algorithm), which is robust and has been used by over 100K people. The focus is on the usability of Google Sheets so that a wider audience finds it easy to navigate Sounds interesting? DM me Please amplify for good karma 🙏show more

Holy shit... Microsoft open sourced an inference framework that... runs a 100B parameter LLM on a single CPU. It's called BitNet. And it does what was supposed to be impossible. No GPU. No cloud. No $10K hardware setup. Just your laptop running a 100-billion parameter model at human reading speed. Here's how it works: Every other LLM stores weights in 32-bit or 16-bit floats. BitNet uses 1.58 bits. Weights are ternary just -1, 0, or +1. That's it. No floats. No expensive matrix math. Pure integer operations your CPU was already built for. The result: - 100B model runs on a single CPU at 5-7 tokens/second - 2.37x to 6.17x faster than llama.cpp on x86 - 82% lower energy consumption on x86 CPUs - 1.37x to 5.07x speedup on ARM (your MacBook) - Memory drops by 16-32x vs full-precision models The wildest part: Accuracy barely moves. BitNet b1.58 2B4T their flagship model was trained on 4 trillion tokens and benchmarks competitively against full-precision models of the same size. The quantization isn't destroying quality. It's just removing the bloat. What this actually means: - Run AI completely offline. Your data never leaves your machine - Deploy LLMs on phones, IoT devices, edge hardware - No more cloud API bills for inference - AI in regions with no reliable internet The model supports ARM and x86. Works on your MacBook, your Linux box, your Windows machine. 27.4K GitHub stars. 2.2K forks. Built by Microsoft Research. 100% Open Source. MIT License.show more

Claude Code + Meta Ads CLI is f*cking wild... 🤯 I just replaced 80% of my Meta Ads reporting workflow inside Claude Code. All without logging into Ads Manager. Perfect for DTC brands and creative agencies who are sick of logging into Ads Manager, exporting CSVs, and rebuilding the same dashboards every Monday. This setup eliminates the entire loop: → Plug Meta's official Ads CLI into Claude Code → Type one sentence describing the report you want → Claude pulls the data, builds the artifact, and saves it to your folder → HTML dashboards, comparison tables, written briefs → Run it weekly, monthly, anytime a client asks for something custom No CSV exports. No Looker setup. No copy-pasting numbers into decks. What you get: → A live dashboard with KPI cards, top 10 ad set ranking, daily spend chart, and sortable table — built in 90 seconds → Week-over-week comparison reports with CTR drops and CPC spikes flagged automatically → Creative fatigue audits that flag dying ads before CPAs blow up → One-page executive briefs with winners, losers, action items, and recommendations → Anomaly reports that surface every metric deviation over 25% Built 100% in Claude Code with the official Meta CLI. No third-party connector means no ban risk. I put together the complete playbook with the 15-minute setup and a step-by-step Loom video showing you the full install. Want it for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)show more

Those investing in submarines today may be wasting money.... A Virginia-class submarine, powered by an S9G reactor, has a submerged displacement of around 10,000 tons and costs approximately $4–5.8 billion. Its top speed is over 30 knots. Now imagine a much smaller reactor, with power and weight around 15% of the Virginia's, used solely to continuously recharge a solid-state battery bank. Solid-state batteries weigh about half as much as lithium-ion batteries while offering 2-3x more energy capacity. In practice, this means that with the same battery weight, such a sub could achieve roughly 3x the energy gain, In terms of speed, solid-state batteries deliver double or higher discharge rates (potentially 10–20C vs. 5–10C for lithium-ion), ideal for sustained sprints above 30 knots lasting many days and a cruising speed around 25 knots. All this with 15% less overall weight, much quieter operation on batteries alone, and the same endurance as a conventional nuclear sub. And the cost? A micro-reactor would be 15–25% the price of a conventional one, small, modular, low-temperature/low-pressure. This means that when a more modern reactor is needed, you simply swap the module. A micro-reactor paired with solid-state batteries could make a Virginia-class sub $1.2-1.6 billion cheaper, quieter, and leave far more space for weapons, additional batteries, or crew comfort. That's why this system would put all existing submarines at a disadvantage in terms of cost, space, and stealth. Those not adopting micro nuclear reactors can follow what the Germans, Japanese, and French are doing. The Japanese pioneered lithium-ion batteries with diesel chargers, giving their submarines excellent value for money. The Germans chose a fuel cell AIP system to recharge lithium-ion batteries, while the French opted for a similar Japanese-style approach with a battery configuration allowing up to 80 days endurance, making the new Scorpène highly competitive. Starting around 2030, production will shift to solid-state batteries, tripling the capacity of these conventional submarines and enabling silent navigation at around 25 knots for days,making them superior in stealth and speed to many nuclear submarines currently in service. Submarines powered by solid-state batteries, recharged via micro-reactors, fuel cells, or diesel, will be superior: better armed, cheaper, and stealthier than anything we know today.show more