If you’ve been anywhere near tech Twitter, [Hacker News](https://news.ycombinator.com/item?id=46994003), or [Reddit](https://www.reddit.com/r/technology/) this past week, you’ve probably seen the quote that lit everyone on fire. During Spotify’s [Q4 2025 earnings call](https://newsroom.spotify.com/2026-02-10/spotify-q4-2025-earnings/), co-CEO Gustav Söderström casually dropped that the company’s best developers “have not written a single line of code since December.” The reason? An internal AI… Continue reading

If you’ve spent any time wiring up multi-agent systems, you know the pain. You sketch out a workflow, hardcode agent roles, define handoff patterns, and then pray everything holds together when the task gets messy. Frameworks like LangGraph, CrewAI, and AutoGen all follow this playbook — you, the developer, decide the structure upfront, and agents… Continue reading

If you’ve been anywhere near the data infrastructure space this past week, you’ve probably seen [SurrealDB 3.0](https://surrealdb.com/3.0) pop up in your feed. [VentureBeat](https://venturebeat.com/data/surrealdb-3-0-wants-to-replace-your-five-database-rag-stack-with-one), [SiliconANGLE](https://siliconangle.com/2026/02/17/surrealdb-raises-23m-expand-ai-native-multi-model-database/), [Tech.eu](https://tech.eu/2026/02/17/surrealdb-secures-23m-and-launches-surrealdb-3-0-to-address-ai-agent-memory-challenges/), [EU-Startups](https://www.eu-startups.com/2026/02/london-based-surrealdb-raises-additional-e19-million-to-scale-multi-model-database-for-ai-applications/) — everyone covered it. And honestly, I get why. The pitch is bold: replace your entire multi-database RAG stack — vector DB, graph DB, relational DB, document store, cache… Continue reading

There’s a bottleneck in AI that nobody outside the industry talks about much: getting human feedback at scale. Sure, we’ve got massive compute clusters and clever model architectures, but when it comes to actual humans evaluating AI outputs — ranking which image looks more natural, which text sounds better — the process is painfully slow… Continue reading

There’s something deeply satisfying about watching someone strip a problem down to its absolute bare bones. That’s exactly what [zclaw](https://github.com/tnm/zclaw) does — it takes the concept of a personal AI assistant and squeezes it into an 888KB firmware image running on an ESP32 microcontroller. The whole thing is written in C, and when you look… Continue reading

There’s a lot of noise in the AI coding space right now. Every other startup claims they can write code for you, autocomplete your functions, or “vibe code” an entire app into existence. But [Code Metal](https://www.codemetal.ai/) is doing something fundamentally different — and the market clearly agrees, because this company just went from a $250M… Continue reading

If you’ve spent any time watching AI coding assistants fumble through a large codebase, you know the frustration. They miss dependencies, break call chains, and suggest changes that look fine in isolation but wreck something three folders away. The core problem? These tools treat your code as flat text in a context window. They have… Continue reading

If you’ve been anywhere near medical AI Twitter this week, you’ve probably seen DeepRare popping up everywhere. The system just had its [research paper published in Nature](https://www.nature.com/articles/s41586-025-10097-9) on February 18th, and the coverage has been intense — [MedicalXpress](https://medicalxpress.com/news/2026-02/deeprare-ai-outperforms-doctors-rare.html) ran with the headline “DeepRare AI outperforms doctors on rare disease diagnosis,” and [CGTN](https://news.cgtn.com/news/2026-02-19/-Agentic-AI-system-delivers-high-accuracy-in-rare-disease-diagnosis-1KSXg0DechO/p.html) followed up almost… Continue reading

There’s a lot of noise in the AI space right now, but every once in a while something comes along that feels genuinely different. [Goodfire](https://www.goodfire.ai/) is one of those. They’re an AI interpretability lab that essentially reverse-engineers neural networks — cracking open the black box to figure out what’s actually going on inside. And honestly,… Continue reading

There’s something deeply satisfying about watching a 70-billion-parameter model run on hardware that was never supposed to handle it. That’s exactly what [ntransformer](https://github.com/xaskasdf/ntransformer) pulls off — Llama 3.1 70B on a single RTX 3090 with just 24GB of VRAM. No multi-GPU rigs, no cloud rentals, no $10k server builds. Just your gaming card and a… Continue reading