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Agentic SDLC at Uber — Uday Kiran Medisetty & Adam Huda, Uber
More than 70% of pull requests at Uber now come from local or cloud agents, and lines of code per engineer has doubled year over year.
Research archive, page 59
Browse entries 1393–1416 of 1531. Return to the first page to search and filter the complete collection.
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The Missing Layer: Design Taste in AI Agents — Hassan El Mghari, Together AI
Most people can spot a vibe coded app in two seconds and cannot say why. Hassan El Mghari names it: the purple gradient background, italics in the header, a scroll to explore prompt nobody asked for, all caps pills with wide letter spacing, too many emoji.
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Building Agents Is Trivial Now, Context Is the Next Frontier — Jeff Ng, Unblocked
An agent built to enrich Linear tickets read a report that time to first character in Unblocked's own QA pipeline had gone from hundreds of milliseconds to three or four seconds, and recommended turning async dispatch back on. The recommendation was wrong.
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How I automate my own job at Hugging Face using agents — Niels Rogge, Hugging Face
Thousands of GitHub issues, opened automatically, have produced exactly two negative replies.
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Prototyping as Leadership: How a CTO Ships with AI Agents — Hursh Agrawal, The Browser Company
Fifteen recurring meetings a week, seven direct reports, a toddler at home, and somewhere inside that Hursh Agrawal ships between two and ten pull requests. It was not possible a year ago.
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The Last Human Code Review: Building Trust in AI-Generated Code — Itamar Friedman, Qodo
If you are shipping AI generated code faster than your humans can review it, Itamar Friedman's position is that you are inside the problem rather than ahead of it.
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Coding Agents Don't Scale Themselves. Neither Do Your Teams. — Patrick Debois, Tessl
In 2009 people told Patrick Debois that continuous delivery was crazy. He hears the same thing now about the dark factory, and reads it the same way: not that the technology cannot work, but that the organization is not set up for it yet.
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Unlock Agent Autonomy: The Runtime for AI-Native Systems — Tushar Jain, Docker
An agent that had quietly emailed him a nightly summary for weeks decided one morning to post it as a pull request instead. Nothing had changed. The model simply judged that publishing would be more helpful.
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Healthcare’s Agent Bytecode: X12 as the Harness for AI Agents — Vasant Kearney, Onlay
Call the payer, open their web portal, and read their X12 feed, and all three can tell you the patient is covered. You treat the patient anyway, and the claim comes back denied because they were not covered at the time. Vasant Kearney's point is that none of those surfaces is ground truth.
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Trading Desks to Clinical Trials: Parallels in Applied Vertical AI — Ayush Bhardwaj, Allos AI
Ayush Bhardwaj could build the agent. What he could not do was tell whether it was any good. He moved from applied AI at a hedge fund to a pharma tech company expecting a different world, and found the job identical, including the wall.
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The Era of Compound Engineering — Kieran Klaassen, Every/Cora
He has not written a line of code this year, and has not read most of it either, yet he ships a full email client that thousands of people trust with their inbox. Kieran Klaassen has been rebuilding Cora alone since January, and the useful part of his account is the sequence of bottlenecks he moved through.
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The Next Game Engine Won't Have a Manual — Arturo Nunez, Nereu
Ask a coding agent for a camera that follows your character and it will reinvent that camera from scratch, every time, slightly differently. Arturo Nunez's diagnosis is that the context sits on the game engine's vocabulary rather than the game's.
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Infra behind Krea 2: How to train and serve at scale — Gabriel Jorge Menezes, Krea.ai
GPU utilization is a lie. It read 100% straight through pretraining while the cluster was nowhere near well used, so Gabriel Jorge Menezes tracks tensor core utilization instead, and watched it climb as training resolution stepped from 128 pixels up to 1024.
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Building an Agentic Video Editor for Mass Consumer — Ekaterina Deyneka, Reelful
Nearly every hand in the room went up when she asked who had recorded video at the conference. Almost none stayed up for who had actually posted any of it.
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Context Engineering in 2026 — Louis-François Bouchard, Omar Solano & Samridhi Vaid, Towards AI
The cheapest configuration they tested was the one sending the most tokens. Across 11 presets run against their open source AI tutor, doing nothing at all to the context beat every compaction technique on recall, cost, and latency at once, and their own production defaults scored worse than leaving the history alone.
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Bringing agents onto the world wide web — Paul Klein IV, Browserbase
When OpenClaw shipped, people started buying Mac minis to run it from home, SSHing in and clearing captchas off a residential IP. Paul Klein IV points out he has yet to see a SOC 2 compliant Mac Mini setup at scale, and that this felt like a reasonable answer is itself the problem.
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Computer-use models will agentify the web, not APIs — Dhruv Batra, Yutori
To learn what a US school district is buying, you file a Freedom of Information Act request. Someone scans the email you sent, puts the scan on Google Drive, and attaches the relevant PDFs. Dhruv Batra's question is whether anyone seriously expects that office to publish an MCP server.
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Improving Agents is a Data Mining Problem — Vivek Trivedy, LangChain
Does your agent get dumber after the first compaction? After the second? You cannot read that off the code, only off the traces, and there are far too many to read yourself.
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Adaption Labs: Gradient-Free Continual Learning — Sara Hooker, Adaption
Fewer than five thousand people in the world know how to train a frontier model at scale, by Sara Hooker's estimate, and that knowledge travels like an apprenticeship rather than a literature.
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Bringing Continual Learning into Enterprises — Samuel Denton, Applied Compute
A Qwen thinking model was taking up to 80 turns to submit on SWE bench. Applied Compute wanted it wrapping up by turn 40 and got the submit tool call rate from 22% to 60% with test pass rate flat.
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Designing Agents (The Floor Is the Frontier) — Ben Hylak, Raindrop
Build the thousand example eval suite everyone tells you to build, switch harnesses, and 80% of it stops meaning anything. Ben Hylak's complaint is that eval advice is still written for the chatbot era, back when you knew the answer to nearly every question a user would ask.
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Agents, codebases, and teams — Aditya Khandelwal, Amazon AGI Lab
Leave agent adoption to individuals and the engineer shipping two PRs a day ends up reviewing the ten that the early adopter ships. They fall further behind, the code they are reading is worse, and they conclude the agents are the problem.
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Lessons from Studying Every Memory System — Shlok Khemani, Independent
A profile ChatGPT keeps on Shlok Khemani says he travelled to Turkey in 2025. He never has. The memory came from conversations where he was choosing between Turkey and Thailand, he went to Thailand, and the profile kept both with overlapping dates.
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LLM Knowledge Bases: a practical guide — Ben Holmes, Warp
Talking runs about 200 words a minute and typing does not, which is why Ben Holmes starts a knowledge base with voice dictation rather than with organization.