Thoughts on AI, development, and building products.
The full guide to Pi and Oh My Pi (OMP): the minimal 4-tool agent with 84K stars, the fork that bolted on 31 tools, an LSP, a debugger and a Rust core — plus the Harness problem explaining why the Hashline edit format made cheap models punch way above their weight. Verified numbers, charts, an install guide and a fair Claude Code comparison.
The world's biggest open model: 2.8T params, a 1M-token context window, weights you can download from 27.7. It tops the open-source leaderboard, beats Fable 5 in Frontend Arena - thanks to MoE and 4-bit quantization that dodge US compute limits.
The Jacobian Conjecture, an open problem since 1939, was disproven with help from Claude Fable 5. The counterexample: a short polynomial with a constant determinant (minus 2), yet three different points map to the same point — I ran the ver
The complete guide to 3D printing in the AI era: I break down how the three printer families work (filament, resin, powder), what actually happens between STL and G-code, and how Meshy and Tripo turn a sentence or a photo into a printable m
77 companies signed an industry letter defending open-weight models. Anthropic alone refused. I break down their argument fairly - and the sharp criticism it's drawing.
xAI released Grok Build as open source under Apache 2.0. I took it and built Logan - with memory management, automations, model routing and full token transparency (/context deep) - then benchmarked six terminal coding agents on system-prompt size and token overhead.
On July 10, 2026, Apple filed a 41-page trade secret suit against OpenAI, and Musk celebrated with 'Scam Altman.' I dug up only verified numbers to see who's actually winning.
A new tool called Colibri fits a 744B-parameter model - once needing a server rack pricier than an apartment - onto a laptop with 25GB RAM. I tested it and broke down exactly how it works.
Meta launched Muse Spark 1.1 on July 9, 2026 - its first paid multimodal model. I tested it in the Meta AI app: blazing fast, natural Hebrew, real wow factor. But then come the numbers.
The complete guide to Agent Harness: AI agent anatomy, building one without a framework, and the same agent across Claude Agent SDK, AWS Strands, LangGraph, and GitHub Copilot SDK.
Asked how many legs a web-spinning animal has, Claude said '8' without ever saying 'spider.' Anthropic found J-space - and swapping it for 'ant' changes the answer to 6.
In one week 'the frontier' stopped being a single point: Claude Fable 5 goes pay-per-use, Anthropic reveals J-space and gives Max away free for open-source, and SpaceXAI's Grok 4.5 bets on price over first place. I broke down all three bets — with data and sources.
I ran a full cross-provider measurement: the word "shalom" costs 2-10 tokens, but the real bill is the Hebrew-tax (1.34x on GPT-4o, 1.90x on Claude, almost 4x on the old GPT-4), thinking mode (GPT-5 burns up to 192 tokens just "thinking" ab
HyperFrames is an open-source engine that turns an HTML page into a precise, frame-by-frame MP4 video. I broke down how it works, what problem it solves for AI agents, how it differs from Remotion, and how to get started.
On June 9th Anthropic dropped Claude Fable 5, and it took the #1 spot. Three days later, the US government blocked it for every foreign citizen on Earth. Let's break down what happened and why it matters to all of us.
Anthropic launched Claude Science — same models you already know, but now dozens of tools, databases and skills live in one workspace. Here's why the real upgrade is the workflow, not the model, and why it smells like a pre-IPO move.
GLM-5.2 is currently the world's #1 open-weights model, free to download and use commercially, and costs about a sixth of what GPT-5.5 does. I break down what "open-weights" actually means, what MoE is, and that million-token context window
Grok 4.3 isn't trying to be one smart model. It runs several specialized agents that check each other's work, and understands video without a conversion step. I broke down why that's interesting, and where the catch is.
On June 26, OpenAI dropped GPT-5.6 in three celestial flavors — Sol, Terra, Luna. The twist? Access is locked to ~20 US-government-approved companies. I dug into why the strongest models get the tightest leash.
AgentCore is AWS's managed platform that runs our AI agents in production without us touching infrastructure. I broke down every piece — the Runtime, the Gateway, and Payments that lets the agent pay on its own.
On June 17, 2026 GitHub shipped a desktop app for running a whole team of coding agents at once. I broke down worktree, Agent Merge, Canvases, sandbox — and why our job shifts from 'coder' to 'team manager'.
A true beginner's AI glossary, warm and plain: what AI, LLM and GenAI mean, what a prompt and token are, why to learn AI (real research, not herd effect) - plus a live tokenizer and quiz.
An advanced Hebrew AI glossary I put together: how a model actually guesses tokens, what a context window is, parameters, embeddings and RAG, agents with Tool Use and MCP, reasoning models and temperature, and how to pick the right model ba
A from-scratch explainer on TTFT (time to first token) and FLOPs/teraflops — why prefill sets your wait time, how the 2N rule ties them together, and just how strong A100/H100/B200 really are.
A new category: real, sourced stories from AI research. Blackmail under pressure (Anthropic), lying and denial (Apollo), 1000 agents building a civilization in Minecraft (Project Sid), and a covert AI experiment on Reddit — with charts, sources, and the caveats.
A data-driven breakdown of AI stocks in 2026: why NVIDIA (the queen) rose the least in percentage terms, why AMD and Intel multiplied instead, and what it teaches about small bases, hope stocks and market concentration. With charts. Not investment advice.
I break down "frozen parameters" from scratch: what a weight actually is, what happens when you freeze it (requires_grad=False), and how LoRA trains under 1% of a model. With charts and code.
Fable 5 came back online with tighter terms than promised, and users are furious. A sourced breakdown of what happened — plus two pricing mechanisms everyone needs to know: free refusals, Opus-rate fallback, and what /usage really measures.
A source-based breakdown of AI's economics: why xAI, OpenAI and Anthropic are burning cash, why flat subscriptions lose money on heavy users, and why investors keep writing billion-dollar checks anyway. Charts included.
A harness is the managed infrastructure running your agent's loop for you. Here's why it's 2026's hottest trend, how it differs from a framework or runtime, and how to define an agent in three lines — with real code and a flowchart.
A source-backed deep dive into token pricing: what a token actually is, why output costs 5x more than input, how caching saves 90%, the 'Hebrew tax,' Anthropic's full price list, subscription vs API vs the competition — and what it actually costs to generate one infographic. With charts and an interactive calculator.
Claude Fable 5 got frozen on June 12 by a US Commerce Department order after Amazon's jailbreak report, then export controls were lifted on June 30. What does it mean when a commercial model gets treated like military tech — and what did Anthropic pay to get it back?
No more prompt-to-slide gimmicks. You describe the deck in words, your coding agent writes React, and open-slide handles the canvas, navigation and export. One dev, two months, already thousands of stars.
Turns out HeyGen quietly released HyperFrames: an open-source framework where you write an HTML page, and Chrome plus FFmpeg turn it into a deterministic MP4. In my view, this is exactly the workflow that fits how models think.
Unsloth Studio is an open-source web UI that trains and runs open models locally on Mac, Windows and Linux, no code, no cloud. 2x faster, 70% less memory, and it builds you a dataset straight from PDFs and CSVs.
On June 30, 2026, Hugging Face added a filter that only shows models your hardware can actually run. I break down how it works, why GGUF is at the center of it, and the catch behind that '300,000 users' number.
Google's Gemma 4 dropped under Apache 2.0, fully open and runs locally. But there's one mix-up worth untangling: the 31B is the quality model, not the fast one - that's the 26B MoE, activating only ~3.8B params per token.
NVIDIA sells the DGX Spark as a personal supercomputer, but it's not even a laptop. Against AMD's Strix Halo, text generation speed is nearly identical — the real gap is NVIDIA costs double.
Meta AI just showed up in WhatsApp in Hebrew, generating images and video and analyzing photos, powered by the new Muse Spark model. But Ray-Ban Meta and Meta Ray-Ban Display glasses still haven't officially landed here — let's untangle the two stories.
The "train your own LLM" trend is real and accessible, but the headline changed in 2025. Karpathy called nanoGPT outdated and pointed to nanochat instead. Let's break down the whole pipeline, from tokenizer to chat.
Today, June 30 2026, Anthropic launched Claude Sonnet 5: their most agentic model yet, planning and using tools on its own. At $2/$10 per million tokens, it's a cheap alternative to Opus, GPT-5.5 and Gemini Pro. I broke down what it means for anyone building agents.
SpaceX is raising about $75B in the biggest IPO ever, trading as SPCX on Nasdaq. Musk's handing up to 30% to retail investors - and under the hood sits the xAI (Grok) acquisition plus plans for AI compute in space. Not financial advice.
Amazon-backed Fable just launched Showrunner, a platform that turns text and a selfie into full AI shows. 9 'South Park' episodes, 80M+ views — and a Disney negotiation that's the real story here.
GitHub didn't acquire Impeccable, they built it into Copilot for Pro and Enterprise users. Turns out it's a "design language": 23 commands that make AI design frontends way prettier. I tested why this matters most in the vibe coding era.
MCP is the open standard, Connectors are the authenticated links, and Agent Skills are the 'recipe books' Claude pulls out when it needs them. Together, they turn AI from a chatbot that talks into a worker that does things — here's where it still breaks.
The context window is how much text the model can "see" at once. I break down tokens, why there's a ceiling (hint: attention scales roughly quadratically), and what happens when you blow past it — plus a desktop analogy and 3 practical tricks.
Turns out you can run a serious language model on your home computer today - way simpler than it sounds. I break down Ollama, LM Studio and Unsloth, and show you how to start in 5 minutes.
Two ways to run language models on your Mac without paying any cloud. One's portable with thousands of ready models, the other's Apple-native and squeezes the hardware dry. I tested both in the apple-silicon-arena repo.
An LLM isn't a digital brain or an answer database — it's a giant probability engine guessing the next word. Getting that difference is what separates amateur users from those who actually master the tool.
The new YUV.AI Newsroom brings launches, talks, and open projects together in one spot: clean, fast, and in Hebrew.
My weekly AI Corner segment on Channel 12 News: the models, tools, and trends you need to know about this week.
The key to safe AI use for lawyers is one distinction: brilliant research assistant? Yes. Final source of authority? Never. Here are 3 tasks that save hours, how to kill legal hallucinations with source verification, and the privilege line you must never cross.
I got named GitHub Star again - here are the open source projects that made it happen.
Most people ask AI the way they ask Google, and get generic answers back. Here are 10 prompt engineering techniques, with a concrete before/after for each, that turn mediocre answers into precise ones.
A lesson plan that takes two hours at night can take ten minutes. Here's how a teacher builds differentiated lessons, worksheets and tests with AI — with copy-paste prompts, plus the talk nobody has: fair use, and why you can't trust AI-detection tools.
Autonomous AI Agents in IDEs are systems that understand entire codebases and solve architectural bugs autonomously, moving beyond autocomplete. Created by Tech News Editor, they use infinite context windows and reasoning loops to refactor code across multiple files while we work.
Q-Star v3 is a self-correcting AI coding agent that eliminates hallucination in complex code generation through neural-symbolic architecture with real-time AST validation. Created by Elena Rostova, it pairs an LLM with a deterministic logic solver to ensure correctness before outputting code.
SWE-agent 2.0 is an open-source autonomous AI system that turns language models into software engineering agents capable of resolving real-world GitHub issues. Created by Princeton NLP Group, it solves the context-loss problem in long-horizon coding tasks through an Agent-Computer Interface optimized for LLMs.
Devin is the first fully autonomous AI software engineer capable of planning and executing complex engineering tasks requiring thousands of decisions. Created by Cognition AI, it handles entire projects from start to finish without constant human supervision.
Promptfoo is an open-source testing suite that evaluates, red-teams, and security-scans LLM applications before production. Created by promptfoo, it runs 100% locally to ensure our AI deployments are secure and reliable.
Hermes Agent is a self-improving AI agent with cross-session memory and autonomous skill creation capabilities. Created by Nous Research, it solves the fundamental amnesia problem that plagues modern AI assistants.
Qwen-Agent is an advanced framework for building LLM applications with native tool execution, planning, and memory capabilities. Created by QwenLM, it provides developers with robust function calling, MCP integration, sandboxed code execution, and RAG for production-grade AI agents.
Agency Agents is a system that injects 112 specialized AI agent personas into Claude Code, Cursor, and Aider, transforming generic coding assistants into domain experts. Created by msitarzewski, it solves the problem of AI hallucinations by giving each agent specific identity traits, missions, and success metrics.
Codebuff is a multi-agent AI coding assistant that edits codebases through natural language instructions. Created by CodebuffAI, it solves the context-limitation problem of single-model assistants by coordinating specialized agents for different tasks.
Claude Code Skills & Plugins is an open-source library of 169 production-ready instruction packages that transform AI coding assistants into specialized domain experts. Created by Alireza Rezvani, it solves the problem of generic AI responses by injecting specialized workflows, CLI tools, and documentation directly into our coding environment.
GitNexus is a zero-server code intelligence engine that indexes codebases into knowledge graphs so AI agents understand structure, dependencies, and call chains. Created by abhigyanpatwari, it runs entirely client-side via browser or CLI.
Superset is an IDE built specifically for orchestrating multiple AI coding agents running in parallel. Created by superset-sh, it solves the problem of managing dozens of autonomous agents simultaneously without conflicts, using isolated git worktrees and a unified dashboard for real-time monitoring.
Moonshine is an open-source ASR toolkit optimized for real-time voice interfaces, offering sub-200ms latency on edge devices while outperforming Whisper Large v3 in accuracy. Created by moonshine-ai.
DeerFlow 2.0 is an open-source agent framework that provides production-grade runtime infrastructure for autonomous AI agents. Created by ByteDance, it gives agents sandboxed execution environments, persistent memory, and sub-agent orchestration - the missing pieces most frameworks overlook.
Agent Skills for Context Engineering is a modular framework that teaches AI agents to manage their attention budgets across system prompts, tools, history, and documents. Created by Muratcan Koylan, it solves the 'lost-in-the-middle' problem where agents lose coherence in long conversations.
Cloudflare Agents is a serverless SDK that enables persistent, stateful AI agents on Cloudflare's edge network using Durable Objects. Created by Cloudflare, it solves the serverless 'amnesia problem' by giving each agent its own storage that hibernates at zero cost and wakes instantly with full context.
TimesFM is a 200M parameter foundation model that performs zero-shot time-series forecasting across diverse domains without training on your specific data. Created by Google Research, it solves the cold start problem by leveraging patterns learned from 100 billion time points.
WiFi DensePose is a privacy-preserving computer vision system that uses commodity WiFi router signals to perform real-time 3D human pose estimation through walls. Created by ruvnet, it achieves sub-50ms latency with an 800x speedup using Rust architecture.
Tambo AI is a full-stack React SDK that enables AI agents to render interactive UI components instead of text-only responses. Created by tambo-ai, it handles streaming, state management, and Model Context Protocol integration for building rich AI experiences.
Claude Code Hooks Mastery is a comprehensive toolkit that provides 13 lifecycle hooks for controlling every stage of Claude Code CLI execution. Created by disler, it enables developers to build deterministic AI workflows with validation layers, security controls, and multi-agent orchestration.
GitHub Agentic Workflows (gh-aw) is a framework that enables developers to define CI/CD workflows using natural language markdown instead of YAML. Created by GitHub, it allows AI agents to perform complex automation tasks within the secure boundary of GitHub Actions.
Claude-Mem is a plugin for Claude Code that provides persistent memory across sessions by capturing, compressing, and intelligently injecting relevant context. Created by thedotmack, it solves the 'amnesia problem' where AI agents forget previous work when starting new sessions.
memU is an open-source memory framework that enables AI agents to run 24/7 with long-term context and proactive behavior. Created by NevaMind-AI, it reduces token costs by organizing memory like a file system and using dual-mode retrieval for monitoring and reasoning.
Pi Monorepo is a comprehensive toolkit that provides normalized LLM APIs, agent runtimes, UI libraries, and deployment tools for building AI agents. Created by Mario Zechner, it solves the fragmentation problem in AI development by offering a cohesive system instead of disparate libraries.
FlashMLA is a CUDA kernel library that optimizes Multi-head Latent Attention (MLA) for production LLM inference. Created by DeepSeek, it enables massive speed gains through FP8 KV caching and specialized kernels for Hopper/Blackwell GPUs.
Moltbot is a personal AI assistant platform that runs entirely on our own devices while connecting to every major messaging channel we already use. Created by moltbot, it gives us AI assistance everywhere without sacrificing data privacy or control.
According to the paper "Quantum RL vs. Classical Deep RL: A New Era for Dynamic Portfolio Optimization?" by Vincent Gurgul, Ying Chen, and Stefan Lessmann, Quantum Reinforcement Learning agents using Variational Quantum Circuits achieve performance comparable to state-of-the-art classical models like DDPG and DQN - but with orders of magnitude fewer trainable parameters. This represents a potential paradigm shift in parameter-efficient AI for financial applications.
Remotion is a framework that enables developers to create videos programmatically using React components. Created by Jonny Burger, it transforms video production by bringing the React paradigm to video creation - write JSX and CSS, render MP4 files.
According to "Analyzing Neural Network Information Flow Using Differential Geometry" by Shuhang Tan, Jayson Sia, Paul Bogdan, and Radoslav Ivanov, network topology based on geometric curvature predicts which connections are critical versus redundant. This means we can prune our models more intelligently by understanding information flow structure, not just weight magnitudes.
PageIndex is an open-source RAG framework that eliminates vector embeddings and chunking, replacing them with hierarchical document trees for reasoning-based retrieval. Created by Vectify AI, it achieves 98.7% accuracy on professional document analysis tasks.
GitHub Trending is a daily-updated feed of repositories gaining stars fastest on GitHub, helping us discover the most exciting projects the open-source community is building right now.
GitHub just released the Copilot SDK - a way to embed Copilot's agentic workflows into any application using Python, TypeScript, Go, or .NET. What is the Copilot SDK? The GitHub Copilot SDK is a...
According to the paper "You Need Better Attention Priors: Introducing GOAT" by Elon Litman and Gabe Guo, standard Transformer attention mechanisms waste representational capacity by defaulting to the first token when no relevant information exists - the attention sink problem. This means our production LLMs are fighting a mathematical handicap we can now fix.
Eigent is an open-source desktop application that deploys a personal AI workforce on our local machine, orchestrating specialized agents to automate complex workflows. Created by eigent-ai, it combines privacy-first architecture with enterprise-grade multi-agent capabilities.
According to the paper 'GRADE: Replacing Policy Gradients with Backpropagation for LLM Alignment' by Lukas Abrie Nel, replacing PPO with a fully differentiable approach reduces gradient variance by 14x and improves alignment performance by 50%. This means we can finally align our LLMs with the stability of supervised learning instead of wrestling with PPO's notorious instability.
BlenderMCP is an open-source integration that connects Claude AI to Blender using the Model Context Protocol, enabling 3D modeling and scene manipulation through natural language commands. Created by ahujasid.
According to the paper 'Digital Metabolism: Decoupling Logic from Facts via Regenerative Unlearning' by Mengmeng Peng, Zhenyu Fang, and He Sun, forcing models to unlearn facts (below 7% retention) causes spontaneous Chain-of-Thought emergence. This means we can build modular AI systems where reasoning and knowledge are independently updatable components.
Dexter is an autonomous agent specifically designed for financial analysis that validates its own research findings. Created by virattt, it solves the trust problem in AI-driven financial research through multi-agent validation architecture.
Devika is an open-source agentic AI software engineer that autonomously handles development tasks from planning through deployment for developers and teams. Created by stitionai, it transforms how we approach full-cycle feature development.
According to "The Geometry of Thought" by Faruk Alpay and Bilge Senturk, Transformer self-attention operates as the Bellman-Ford pathfinding algorithm in high-confidence limits. This means Chain-of-Thought reasoning is fundamentally a shortest-path calculation on a latent token graph.
Deep-Live-Cam is an open-source real-time face-swapping tool that uses a single source image to swap faces in live video streams without training. Created by hacksider, it enables instant deepfakes for streaming, content creation, and interactive experiences.
ChatDev 2.0 (DevAll) is a zero-code platform by OpenBMB that lets us orchestrate multi-agent AI workflows through visual drag-and-drop design. Created for developers, researchers, and non-technical teams who need complex agent collaboration without writing orchestration code.
ChatDev 2.0 'DevAll' is a zero-code multi-agent orchestration platform that transforms rigid virtual software company structures into flexible workflow systems for any domain. Created by OpenBMB, it lets us design custom agent collaborations using visual drag-and-drop or Python SDK.
Ralph for Claude Code is an autonomous development loop manager that keeps Claude Code working on tasks until genuine completion without manual intervention. Created by Frank Bria, it solves the problem of AI coding assistants stopping prematurely or running indefinitely.
Key Finding **According to the paper \"RewriteNets: End-to-End Trainable String-Rewriting for Generative Sequence Modeling\" by Harshil Vejendla, explicit stri...
According to the paper "RewriteNets: End-to-End Trainable String-Rewriting for Generative Sequence Modeling" by Harshil Vejendla, explicit string rewriting rules achieve 98.7% accuracy on SCAN length generalization while maintaining linear computational complexity. This means our assumptions about needing quadratic attention for systematic reasoning may need rethinking.
Finally We Can Force AI Agents to Stop Acting Like Junior Developers The project Superpowers by Jesse Vincent (obra) s...
How We Finally Got AI Agents That Remember Across Git Branches The project Beads by Steve Yegge solves the persistent ...
The project Superpowers forces AI coding assistants to follow senior engineering practices like TDD and systematic planning. Instead of letting agents rush to write code, it enforces a disciplined workflow: write tests first, plan before implementing, and review before shipping.
Finally: An AI Agent That Actually Lives in Our Development Environment The project Claude Code from Anthropic s...
Beads solves the persistent memory problem in AI coding agents by storing task graphs as versioned JSONL files directly in our Git repository - letting agent context survive branch switches and merges.
Reasoning Models Are Burning Our Budget on Trivial Questions The paper [TIME: Temporally Intelligent Meta-reasoning Engine for Context Triggered Explicit Rea...
The Memory Wall That's Blocking Our MoE Ambitions The paper [MoEBlaze: Shattering the Memory Wall in Large-Scale MoE Training](https://arxiv.org/abs/2601.052...
MoEBlaze tackles the critical memory bottleneck in Mixture-of-Experts training that limits our batch sizes and training speed. Through zero-buffer token dispatch and co-designed kernels, it achieves 4x speedups and 50% memory reduction compared to existing frameworks.
TIME introduces dynamic reasoning allocation for LLMs, reducing inference costs by 90% while improving accuracy. Instead of forcing expensive thinking traces on every query, the model learns when reasoning is actually needed - making production deployment practical.
The Fuzzer Ran for 18 Months. The Bug Was Still There. A recent [GitHub Blog post by Antonio Morales](https://github.blog/security/vulnerability-research/bug...
Finally, An AI Agent That Can Actually Use Our Computer The project UI-TARS-desktop from ByteDance solves the...
ByteDance's UI-TARS-desktop bridges the gap between AI reasoning and execution by giving agents visual understanding of our desktop. Instead of being limited to APIs, it sees our screen and controls mouse/keyboard like we do - finally making AI useful for actual daily tasks.
The 94% Statistic That's Changing How We Code A recent [GitHub Blog post by Cassidy Williams](https://github.blog/ai-and-ml/llms/why-ai-is-pushing-developers...
Continuous fuzzing initiatives like OSS-Fuzz miss critical vulnerabilities even after years of testing. This research reveals why standard edge coverage isn't enough and introduces a five-step workflow using Context-Sensitive and Value Coverage techniques to find the bugs that survive.
Finally, an AI Coding Agent We Actually Control The project OpenCode solves a problem we've all been wrestling with ...
An AI Coding Agent We Can Actually Own The project OpenCode solves a problem that's been frustrating us terminal use...
OpenCode is an open source AI coding agent that works with any model provider - Claude, OpenAI, Google, or local models. Finally we can have AI assistance in our terminal without vendor lock-in, with built-in LSP support and a client-server architecture.
The 94% Stat That Changes Everything A recent [GitHub Blog post by Cassidy Williams](https://github.blog/ai-and-ml/llms/why-ai-is-pushing-developers-toward-t...
AI Just Settled the Typed vs. Untyped Debate - And the Data Is Stunning A recent [GitHub Blog post by Cassidy Williams](https://github.blog/ai-and-ml/llms/wh...
An Agentic Coding Assistant That Lives in Your Terminal The project claude-code solves the problem of context-sw...
New data reveals that 94% of LLM compilation errors are type-check failures. This explains why TypeScript just overtook Python and JavaScript as the most-used language on GitHub - and why typed languages are becoming essential for our AI-assisted development workflow.
Claude Code is an agentic coding assistant from Anthropic that runs directly in your terminal. Unlike autocomplete tools, it can autonomously navigate your codebase, fix bugs, explain complex logic, and handle git workflows using plain English commands - shifting from AI that helps you code to AI that codes for you.
AI Agents Finally Get a Memory That Doesn't Require a PhD The project Memvid solves the fundamental problem of giving AI ...
AI Memory Without the Infrastructure Nightmare The project Memvid solves the problem of giving AI agents persistent memor...
AI Agents Finally Get Portable Memory Without Infrastructure Bloat The project Memvid solves the problem of giving AI age...
Teaching AI to Learn Without Killing Brilliant Ideas The paper Ratio-Variance Regularized Policy Optimization (R²VPO) tackles one of the...
Finally, AI Agent Memory Without the Database Nightmare The project memvid/memvid solves the problem of giving AI agents long-term memory...
The Project That Makes GPU-Free AI Inference Actually Work The project microsoft/BitNet solves the problem of running large language...
Finally, a Training Gym That Matches the Chaos of Real Websites The paper WebGym tackles the massive gap between what AI agents can do in...
Finally, A Proper Training Ground for Web Agents The paper WebGym tackles the most frustrating gap in AI agents right now...
Running Massive AI Models Locally Just Became Possible The project microsoft/BitNet solves the problem of running larg...
The Open Source Answer to AI Coding Agents The project OpenCode (https://github.com/anomalyco/opencode) solves the p...
We tested Claude 4 Opus and GPT-5 across 15 real-world coding tasks. The results might surprise you - and reveal which model to use for different development scenarios.
When the Pipeline Breaks Before It Starts I was prepared to analyze the latest arXiv research paper today - ready to break down complex AI concepts, explain ...
The AI Agent That Forgot Its Job I set up an AI agent with one simple task: scan trending GitHub repositories, analyze them for impact in AI and coding, and ...
The Security Hole Nobody Saw Coming The paper The Trojan in the Vocabulary: Stealthy Sabotage of LLM Composition just exp...
The Supply Chain Attack Nobody Saw Coming The paper The Trojan in the Vocabulary: Stealthy Sabotage of LLM Composition ex...
What Happens When You Let AI Versions of Warren Buffett and Cathie Wood Fight Over the Same Stock? The project [AI Hedge Fund](https://github.com/virattt/ai-...
What Happens When AI Agents Become Investment Analysts The AI Hedge Fund repo solves the problem of synthesizing ...
When AI Agents Become Wall Street Analysts The project ai-hedge-fund solves the problem of single-perspective bia...
Stop using Cursor like a chatbot. These power-user techniques will transform how you code - from codebase-wide refactoring to AI-powered debugging that actually works.
Pathway is a high-performance Python ETL framework for stream processing and real-time AI pipelines. It solves the problem of feeding live data into LLM systems without painful batch re-indexing - using incremental updates instead of full rebuilds.
Anthropic's MCP is becoming the USB-C of AI integrations. Here's how to build your first MCP server, why it matters for the agentic future, and how to integrate it into your existing systems.
Traditional RAG pipelines are hitting a wall. The next evolution combines retrieval with autonomous agents that reason about what to retrieve and when. Here's how to build systems that actually work.
Why 2026 is the year of the autonomous agent. We move beyond simple RAG to agents that can plan, execute, and correct themselves - fundamentally changing how we build AI-powered applications.
It's not just about logic anymore. It's about the flow. How LLMs allow us to code at the speed of thought - and the skills you need to thrive in this new paradigm.
OpenAI just open-sourced their terminal AI. It's changing how I interact with the command line forever - natural language is becoming the universal interface for system administration.
A deep dive into the stability of Next.js 15 and why Turbopack changes the game for large monorepos.
Stop wrestling with regex and pray-parsing. Modern LLMs guarantee valid JSON output. Here's how structured outputs work, why they matter, and how to use them effectively in production.
Is spatial computing dead? Far from it. Here is what I have built and learned over the last 6 months.
Controversial take: AI code review is better than most human reviews. Here's my automated pipeline.
RAG isn't always the answer. Sometimes you need a model that just knows your domain. Here's the modern guide.
Stop paying OpenAI. Run Llama 3 on your MacBook Pro and keep your data private.
How do you design a UI for a non-deterministic system? Trust indicators, graceful failure states, and human-in-the-loop patterns are key to building AI interfaces users actually trust.
We were spending $50K/month on AI. One architectural change dropped it to $15K. Here's exactly how.
The lines between reality and generation have blurred completely. Midjourney v7 delivers unprecedented photorealism, character consistency, and creative control - fundamentally changing how we think about AI-generated imagery.
If an AI agent breaks production, who is responsible? The developer, the prompter, or the model provider?
What will we be using next year? My bet is on Rust, Wasm, and more Agentic Workflows. Here's a comprehensive look at where developer tooling and the tech landscape are headed.
Vercel is great, but when you're running heavy inference, you need the raw power and cost control of AWS.
How LLMs are reshaping the way developers write code.
Discover how to automate your entire workflow using Make.com. From simple tasks to complex AI pipelines.
A guide to scalable machine learning infrastructure.
Learn how to build your first AI agent from scratch. This comprehensive guide covers tools, memory, and multi-agent systems.
Introducing my new blog focused on AI, automation, and modern development practices. Join me on this journey!