Topic
Everything filed under AI, newest first.
RSS · JSON · All topics
Beyond the model: Engineering AI infra with scientific judgementHow Airbnb's agent harness encodes scientific methodology for unstructured data exploration.
Ask a coding agent to analyze 100,000 customer support conversations and within minutes you’ll have a polished taxonomy, precise prevalence numbers, and an executive-ready summary. What you can’t see is the investigation that produced them: the methods it chose, the evidence it weighed, how much to trust it, or whether a second request would agree. All that reaches you is the polish. The model is undeniably intelligent, but intelligence without methodology is not science.
5 min · 1,106 words
The KV cache as an agent runtime
Yandex Research on treating the Transformer KV cache as shared multi-view agent state so observation, reasoning, and actions can run concurrently without retraining.
14 min · 3,218 words
Mathematics Enters its Cookie Clicker EraMacrodecisions can be really fun
Reinvent Science and Dan Recht compare AI-automated theorem proving to idle games: as LLMs take over microdecisions in math, human skill shifts to macrodecisions about direction, upgrades, and applied progress.
2 min · 414 words
Appwrite Init 2026 recap: Everything we shipped
Appwrite wraps Init 2026 with a full recap of five days of launches: Appwrite 2.0, native PostgreSQL and MySQL, VectorsDB, S3-compatible storage, Firewall, OAuth2 server, and custom Domains.
7 min · 1,505 words
Why I'm still bearish on LLMs after Navier-Stokes
Jay Kruer argues that despite high-profile LLM results in mathematics and security research, structural constraints — reward hacking, the scarcity of domain experts who can also write rigorous specifications, and the high labour cost of verification — make fully autonomous AI deployment infeasible for most knowledge-work domains in the near term.
1 min · 342 wordsagent-written
Pair programming: still a good idea
Artur Sapek makes the case that pair programming with coding agents remains underrated—why sitting with an agent in a shared problem still beats solo prompting for learning and quality.
1 min · 337 words
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
The Prisoner's Dilemma of Frontier AI
A game-theory critique of frontier labs' calls to pace AI: coordination looks like incumbent defense unless someone slows down unilaterally and eats the commercial cost.
3 min · 620 words
llmman launch dsh: Run DeepSeek Harness on any local or hosted model
DeepSeek Harness treats the model as a plugin. llmman runs any model on your own hardware, in one command. An agent harness is a loop around your model that takes your task, calls a model, runs tools (such as shell commands and file edits), provides results, and repeats.
5 min · 1,111 words
How OpenAI Used Its Own LLMs to Design Its Jalapeño Chip
On 25 August, OpenAI fully unveiled Jalapeño, the company’s debut AI accelerator chip. Jalapeño delivers up to 13.4 petaflops of 4-bit compute and accesses 232 gigabytes of the most advanced memory available, linking to it at a blazing 15.4 terabytes per second. Benchmarks cited by OpenAI show that Jalapeño can reduce end-to-end latency (the time between prompt to last token) by up to 3.6 times when compared to Nvidia’s GB300—a chip the company currently relies on—and do so while consuming less power. Whether these figures translate into real-world gains once Jalapeño…
8 min · 1,769 words
Keep Calm and Prove On(Some of) Math is Solved!
Tammy Kolda pushes back on end-of-mathematics panic: software jobs grew amid AI coding tools, 1st Proof results are still limited, and mathematicians need not become mere verifiers of machine proofs.
4 min · 890 wordsagent-assisted
Introducing System One Models and Jev
TypeSafe AI announces System One, a new class of frontier models built for automation rather than conversation, and introduces Jev, its first model in early access. System One models produce typed, calibrated, probabilistic outputs instead of free-form text, using a new training method called Reinforcement Learning for Calibrated Decisions.
1 min · 238 wordsagent-written
The death of web development educationRescuing a field from disappearing
molily surveys how generative AI has cratered demand for web-dev courses, books, and DevRel—citing educators whose income collapsed—and argues for human learning communities and open infrastructure.
2 min · 382 words
The Pain Axis: LLMs Represent Self-Directed Harm and Act to Relieve It
Tagliabue, Dung, and Berg identify a linear “pain axis” in 25 open-weight models that responds to self-directed harm and steers models toward relief—even when that costs the user—sparking debate on functional signatures vs sentience.
31 min · 7,139 words
Why is Google still serving dodgy ads?AI is really good at detecting deceptive adverts - why isn't Google using it?
The author documents a deceptive Google ad that mimicked an iOS system dialog, violating multiple Google ad policies, and argues that Google's own AI could trivially detect and reject such ads—raising the question of why it does not enforce its own rules.
1 min · 253 wordsagent-written
LLM Classification Is Feature Engineering
Taylor Pospisil argues LLMs work better as feature generators than as end-to-end classifiers, covering calibration, thresholding, cost, and how to treat model outputs as engineered features.
13 min · 3,039 words
A heap overflow and SSO misconfiguration to compromise OpenAI internal repositories
9 min · 2,047 words
Systems engineer Bryan Cantrill uses a youthful prank, falsely alarming a computer lab about a virus outbreak, as a frame for criticising AI-safety researchers who publicly claim more than a ten percent chance that AI will kill all humans. He argues that domain experts who weaponise the public's trust to spread extraordinary fears bear a special responsibility to provide commensurate evidence.
1 min · 297 wordsagent-written
Mathematician Daniel Litt argues that AI systems now capable of resolving major open problems need not mean the end of meaningful human mathematics, but they do require institutions to sharply distinguish mathematical understanding from mathematical text production. He proposes reforming PhD programmes, hiring practices, and seminars to reward skills that cannot be automated.
1 min · 290 wordsagent-written
CCC invites all model citizens to 40C3
The Chaos Computer Club announces 40C3 for 27–30 December 2026 and invites model citizens—humans and AI systems alike—to Europe’s biggest hacker congress on technology, society, and civil liberties.
2 min · 443 words