AI agent benchmarks are broken
Benchmarks are foundational to evaluating the strengths and limitations of AI systems, guiding both research and industry development.
The latest blog posts to take off on Hacker News. 3,091 posts.
Benchmarks are foundational to evaluating the strengths and limitations of AI systems, guiding both research and industry development.
If you ask the new Grok 4 for opinions on controversial questions, it will sometimes run a search to find out Elon Musk’s stance before providing you with an answer. …
Study Shows That Even Experienced Developers Dramatically Overestimate Gains
Released last night, Grok 4 is now available via both API and a paid subscription for end-users. Update: If you ask it about controversial topics it will sometimes search X …
Can Gemini 2.5 replace CNN for object detection?
In my previous post I showed how LLMs struggle with coding in q/kdb+; having immediate feedback loops using the q-MCP server does help a…
At Soundslice, our sheet music scanner digitizes music from photographs, so you can listen, edit and practice. We continually improve the system, and I keep an eye on the error logs to see which images are getting poor results.
Welp, Pocket shuts down tomorrow despite our pleas for it to stay. While migrating1 all of my saved articles, I noticed that I’ve got almost 900 saved articles spanning nearly 7 years. That’s a goldmine of stuff-I-like data! Some quick analysis using xsv2: 𝄢 unzip pocket.zip && xsv headers part_000000.csv 1 title 2…
In many discussions where questions of "alignment" or "AI safety" crop up, I am baffled by seriously intelligent people imbuing almost magic...
Continual learning is a huge bottleneck
A now-deleted report by HonestAGI on GitHub showed a high number of similarities between Huawei’s Pangu and Qwen 2.5.
How I built Context—a native macOS SwiftUI app for debugging MCP servers—almost entirely with Claude Code, and what I learned about building with AI coding agents.
This isn't innovation, it's tyranny
I signed up for Hinge. Holy shit with the boosts.
How local, learnable routers can reduce token overhead, lower costs, and bring structure back to agentic workflows.
As a supporter, I would love not to feel this way
Since the disastrous launch of the RTX 50 series, NVIDIA has been unable to escape negative headlines: scalper bots are snatching GPUs away from consumers before official sales even begin, power connectors continue to melt, with no fix in sight, marketing is becoming increasingly deceptive, GPUs are missing processing…
prompt engineering is nothing but an attempt to reverse-engineer a non-deterministic black box for which any of the parameters below are unknown: - training set - weights - constraints on the model - layers between you and the model that transform both your input and the model's output that can change at any time …
The idea for this article didn't exist in my brain before this morning. But there I was, scrolling the New page and even more tired of all the AI-related Show HN posts than usual. I was confident that their numbers were multiplying and wanted proof. Exactly how much more AI crap is on my lawn compared to last year?
The solution to agentic flows was code all along.
Multiple parallel Claude-Code sessions power a self-improving AI factory where agents write, review, and refine code.
Fresh AI generated startup ideas from the current Hacker News front page. Powered by Claude.
On making use of large thinking models.
Patterns for managing agent context.
A guide to AI and LLM evals for engineers and product managers. Learn how to test AI systems, analyze failures, and improve retrieval, agents, and AI products.
Context Engineering is the new skill in AI. It is about providing the right information and tools, in the right format, at the right time.
LLMs were invented in four major developments... all of which were datasets
the existential crisis we all share imagine this: you've just spent $640 billion training the chonkiest language model known to humanity (lol) and decide...