Как я ускорил TypeScript-типы в 15.7 раза
Иногда TypeScript думает мучительно долго. У меня это вылезло не самым приятным способом: @_chenglou — он работал над React, ReasonML и ReScript, а…
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An AI Capture-the-Flag Tournament: What the Scoreboard Counted
Code: Megapixel99/capture-the-flag In April I ran five games of an AI capture-the-flag tournament between five small open-weight models (1.0B to 2.5…
We hit 99.95% on the LoCoMo memory benchmark. Here's the catch, and why it still matters.
Our CEO Rob Imbeault published a piece on LinkedIn this week about a result our team posted: 99.95% on LoCoMo , the most cited benchmark for long-te…
How I tried to write an article about slow Chinese LLMs
Recently, I've added a bunch of hype-monsters to my AI Werewolf : Kimi K3 Qwen 3.8 Max, Qwen 3.7 Plus, Qwen 3.7 Flash MiniMax M3 Plus the ones I've…
.NET Matrix: взвешенный выбор библиотеки, а не по звёздам на GitHub
.NET Matrix — это новый открытый проект, который сравнивает .NET-библиотеки внутри одной категории по трём аспектам: возможности , скорость и исполь…
AI News Roundup: Grok 4.5 Hits Tesla, Perplexity's Orchestrator Beats Opus, and Meta Undercuts Pricing
Five stories moved the AI-coding world today. None are about a single model winning forever — they are about the ground shifting under who runs the…
Turn the camera away, and the AI's world freezes
Video AI systems consistently fail to track what happens when the camera looks away: when a scene pans away from an object in motion and returns, cu…
Reliable, and still wrong
A large-scale audit of AI-as-judge evaluation — covering over half a million individual judgments — finds that AI judges are consistently reliable b…
Claude Fable 5 Scores 95% on SWE-bench, Then Hands Off to Opus 4.8
The headline number is 95% on SWE-bench Verified. That's the score attached to Claude Fable 5, Anthropic's new general-access model in the Mythos cl…
Cross-Machine Memory Query: About 20 Milliseconds, Most Days
I wrote about hardware benchmarks twice this week. Different problem this time. Same machines. I have a Mac for daily work, a Linux box that runs a…
An AMD GPU Beat My Mac on Llama 8B. The Same GPU Lost on Phi-3.
I wrote a post yesterday about why GPUs barely help small text embeddings at batch=1. Different workload, same machines. This time I ran a local LLM…
Your GPU Probably Isn't Helping Your Retrieval System
Most "just use a GPU" advice is wrong for how anyone actually runs small models. I spent yesterday benchmarking a 33M parameter embedding model acro…
pypdf vs PdfPig: Text Extraction at Scale
Overview PDF text extraction is a common pre-processing step in data pipelines — ingesting research papers, legal documents, or reports before embed…
NetworkX vs CSR + TensorPrimitives: PageRank on 28M Edges
Overview PageRank is the canonical graph algorithm. NetworkX implements it in pure Python — its dict-of-dict adjacency representation means every po…
SurrealDB 3.x by the numbers
Author: Tobie Morgan Hitchcock One engine, multi-workloads, full durability. You can explore the full results, methodology, and per-database breakdo…
What ground truth caught that unit tests missed: 3 real bugs in 9 flagship lint rules
We added a npm run ilb:flagship:smoke gate to the quality script. It's small: for each flagship rule with a labeled corpus, run the rule against vul…
When Generic Benchmarks Fail: Building a Sales-Domain Evaluation Bench from Scratch
By Natnael Alemseged The gap that τ²-Bench retail cannot measure Tenacious is a B2B sales automation company. Its agent produces outreach emails for…