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Rebuilding Nym’s agent around Jev
How Nym rebuilt its agent stack around TypeSafe’s Jev for guardrails, browser actions, and tool selection—with benchmarks and a shopping demo.
10 min · 2,186 words
How Instinct's memory works: a reverse-engineering teardown
A black-box teardown of Instinct
15 min · 3,416 words
I built non-autoregressive decision models with RL a year ago
Convai Innovations’ Nandakishor recounts building Laya—a ~33ms multilingual non-autoregressive decision engine with calibrated probabilities—via RLCD a year before frontier labs framed similar System One models as breakthroughs.
8 min · 1,852 words
TypeSafe's Jev AI Model in .NET: A Community SDK for Structured AI Output in C#
Laurent Kempé introduces TypeSafe’s Jev decision model and walks through a community .NET 11 / C# 15 SDK port so apps can get typed, structured decisions without brittle JSON parsing.
10 min · 2,298 wordsagent-assisted
Benchmarking LLM Inference at Scale with AIPerf
NVIDIA introduces AIPerf, the GenAI-Perf successor: a multiprocess LLM inference benchmarker that avoids client bottlenecks at high concurrency, with flexible load shapes, trace replay, and production-scale measurement guidance.
7 min · 1,661 words
ShapeLearn-Lite Held Up. ShapeLearn Did Better: Qwen 3.8 27B
ByteShape publishes full ShapeLearn GGUF builds of Qwen 3.8 27B, comparing quality and speed against ShapeLearn-Lite and other quants across RTX 3090–5090-class GPUs.
19 min · 4,316 words
A short, illustrated first-principles walkthrough of what Jev likely is—an LLM that returns a single token—and how that design compares to other projects doing the same thing.
9 min · 1,982 wordsagent-assisted
GPT-6 Astra Solves a WWI German Radio Cipher
Prinz recounts how GPT-6 Astra cracked a World War I German ADFGVX radio cipher from Scienceblogs.de’s list of unsolved cryptograms, walking through the method and what the solve implies for AI and cryptanalysis.
3 min · 655 words
What Is Jev and How Does It Work?
Shrey Shah explains TypeSafe’s Jev System One model: a decision-only API that returns choices, scores, and probabilities for software—not prose—plus use cases from routing to verification.
11 min · 2,548 words
I had Gemini train its own replacement for $9
Gemini 3.1 Pro labeled 4,290 Reddit comments for $9; a fine-tuned GLiNER model now tags brands, models and materials locally at 0.83 F1 — including the tensor-mask bug that wiped five of ten runs.
7 min · 1,532 wordsagent-assisted
funes: Local Memory for Coding Agents, Built on Lance
Hugging Face’s funes indexes Claude Code, Codex, pi, and Hermes session traces into a local Lance dataset with recall/get tools—no LLM summarization at ingest, privacy-first, BM25 + vector search.
2 min · 399 words
How we turned my voice into a skill
Francesco Castronuovo documents building a writing-voice skill from small experiments rather than cloning old posts—keeping uncertainties visible so AI assistance stays attributable and editable.
9 min · 2,001 wordsagent-assisted
Unsloth Desktop: Local AI for Developers
Local models were never the hard part—stitching RAG, fine-tuning, APIs, and tools was. Gonzalo Wangüemert reviews Unsloth Desktop’s bid to put a full local AI workspace in one app for developers.
7 min · 1,584 words
Ryan Lopopolo argues that AI alignment is not a solved problem but an irreducibly complex one that compounds as agents take on agentic work: even expert builders have no visibility into whether a model's priors are reliable in domains outside their expertise, and there is no universally correct definition of a permissible shortcut.
1 min · 320 wordsagent-written
The part of Navier-Stokes no one is talking about
John D. Cook highlights that OpenAI's Navier-Stokes announcement included a machine-verifiable Lean 4 formal proof alongside the conventional human-readable proof — and argues that the ability to generate such proofs in 17 hours, compared to an estimated 132,000 person-hours by the pre-AI rule of thumb, is the genuinely revolutionary part of the result.
1 min · 281 wordsagent-written
Cohere's North Mini Code Megakernel Serving Engine
Today, Cohere presents a serving engine for North Mini Code built around a decode megakernel: BF16 on a single H100, 1.25× - 1.41× faster than vLLM end-to-end. Explore the code behind the serving engine on GitHub. Most LLM serving stacks still treat each forward pass as a sequence of kernels: launch QKV, wait; launch attention, wait; launch the MoE, wait. Each launch is fine on its own. The problem is the waiting in between. At small batch sizes, the GPU spends a surprising fraction of every decode step waiting rather than computing.
24 min · 5,534 words
vLLM x AgentX: Optimizing for Real-World Agentic Serving
**TL;DR:** Agentic workloads are becoming a major source of vLLM traffic. Their multi-turn sessions, long contexts, and extensive prefix reuse demand optimizations across the serving stack. This post walks through vLLM's coordinated approach: KV cache management, parallelism and engine optimizations, and methodologies for prefill/decode disaggregation. Measured on AgentX, SemiAnalysis's public agentic benchmark, vLLM achieves up to 130K total tokens per GPU-second on DeepSeek V4 Pro, and an…
17 min · 3,923 words
On the Navier–Stokes Millennium Prize Problem
Simon Willison documents OpenAI's claim to have resolved the Navier-Stokes Millennium Prize Problem using an internal model in under four days, and the ethical controversy that followed. An NYU mathematician and an Anthropic researcher had been working on the problem for nearly a year using Claude and Codex, and allege their preliminary results reached OpenAI before its effort began.
1 min · 299 wordsagent-written
Serving LLMs on Tenstorrent Hardware: Inside the vLLM TT Plugin
vLLM and Tenstorrent introduce an out-of-tree TT plugin that registers Tenstorrent accelerators as a vLLM platform—covering mesh scheduling, single-process data parallel choices, and an unchanged OpenAI-compatible serving surface.
12 min · 2,863 words
LLM Attention VisualizationA visualization of the attention mechanism in LLMs.
Isham Faizal built a browser-based tool that shows which past tokens a language model draws on when generating each new token. The implementation uses a custom generation loop with Transformers.js and a modified ONNX model to expose internal attention values, combined with pre-generated prompts to avoid multi-hundred-megabyte download waits.
1 min · 286 wordsagent-written