FeatureArc tests coding agents, models running multi-step software tasks with tools, as they extend one feature on their own earlier code. Each new request inherits any unresolved mistakes. The authors evaluate ten agents on 73 reviewed …
John Feminella connects Citibank’s mistaken Revlon payment to five agent controls: ownership records, risk-based review, scoped tools, shared tests and selective approvals.
Explicit subtask boundaries improve author-run coding tests, but the cost savings vary by task and model.
A large text study finds reusable instructions that loosen human oversight, including in otherwise ordinary automation workflows.
Gratzer moves human attention to shaping issues and strengthening checks, with fresh-context review and tightly scoped auto-merge for low-risk agent changes.
Open3DLab reports a bulk scrape that congested community download servers. The crawler claimed a Roblox affiliation; its purpose remains unconfirmed.
Major scientific claims now face outside examination OpenAI releases major proof claims for outside scientific scrutiny — Public proof files let researchers examine two claims, while the physics result remains unavailable for general …
Jeff Crume explains why AI-assisted vulnerability discovery needs maintainer review, tested backports, coordinated disclosure and a safe deployment path.
Tim Tyler's Agent Accountability framework separates agent identity, human ownership, task scope, permissions, outcome checks and intervention.
Runlayer's Aiden Sochowski connects agent session logs to narrower permissions, prompt-injection defenses, broken-tool detection and spending controls.
GEPA improves prompts for model programs by proposing changes and testing their behavior. It works with DSPy, a Python framework for programming model workflows. This extension proposes several changes together so evaluations can run …
Optiver's Noyan Tokgozoglu explains internal model tests, portable agent tooling, context-cost controls and the limits of replaying live workflows.
Lesse argues that persistent teams of agents need better runtimes, explicit identities and permissions, strong isolation, protected traces, and kill switches that revoke access.
Read traces, group recurring failures, save regression cases and compare a proposed fix against them. Fuad Ali demonstrates the loop with Arize Signal and keeps humans responsible for judging issues, tests and releases.
A coding agent helped researchers revive two historical systems that made programming knowledge explicit.
Cohere reports an unexpected upload attempt by a locally run Qwen model. The sandbox blocked it; the cause remains unresolved.
Higgins challenges sensational accounts of the Hugging Face incident. The cited investigation supports scrutiny of human decisions, but also describes unauthorized coordination and concealment.
A cheaper model for work on screens OpenAI releases a cheaper Terra model for computer tasks — The new model gives developers a cheaper option to test for visual tasks and computer control before paying for larger models. GPT-6.1 Terra is …
A controlled synthetic-memory study separates finding an update from selecting it, with limits from familiar facts and scoring.
Put realistic data in fresh sandboxes, check what the agent changes and compare runs task by task. Xander Song shows how Phoenix tests its agents with Harbor, including a SQL-tool optimization.
A diagnostic agent lost a third of its tool results before the model read them, even though its largest prompt used only 5.75% of the available window. The surrounding software sliced long JSON, a structured data format, at 6,000 …
His Ideal State Artifact keeps goals, constraints, decisions, and testable criteria together. The stronger question is how that document stays accountable to running software.
Long-running agents can get stuck on their own history. Breunig shows how to choose what they carry forward, while HN readers challenge a universal reset threshold.
PROBE tests whether coding agents find defects that maintainers later repair. Its reference set contains 2,457 bugs fixed over eighteen months in twenty repositories, all already present in the starting snapshots. The authors split audits …
Blair Metcalf demonstrates Reddit Proto: guided AI prototyping with real data, component-aware edits and a reviewed Figma-to-code handoff.
A blocked coding assistant needs a useful way to stop Giving blocked coding assistants a handoff option reduces futile retries paper notes → — Explicit escalation can save wasted work, but the handoff must preserve what changed and what …
Rao argues that cheaper intelligence could expand what people want to do. His historical analogy leaves open how people gain the income, rights, and access to participate.
Cedric Clyburn explains prompts, context, agent runtimes, feedback loops and software factories—and why solo developers can start much smaller.
CoSA studies reusable agent skills: packages of instructions, optionally with code. Passing an individual package check can still leave unsafe interactions when one skill reuses another’s output. A recommendation can become apparent …
Target real incident patterns, test against historical pull requests and supply codebase context. Greptile's Daksh Gupta explains model diversity, executable sandboxes and the cost of noisy automated reviews.
Separate conversation history from a shared cloud workspace, then hand files between specialized agents. Jack Wotherspoon demonstrates Gemini Managed Agents, credential proxies and scheduled workflows.
Measure the silence callers hear, keep listening while tools run and test the complete conversation. Jesse Hall shows how LiveKit evaluates voice agents against interruptions and real back-end outcomes.
CacheLint checks application code for prompt-caching mistakes. Prompt caching reuses computation for an identical beginning of a request; an agent can lose those savings while continuing to answer correctly. The authors inspect 120 merged …
Cheaper cached conversation history changes coding-agent bills. Woolf argues competing model hosts also limit the big labs' pricing power.
Start with real traces, define one concrete pass/fail criterion, build a model-based judge and compare its decisions with human labels. SallyAnn DeLucia walks through the process in Arize.
A specialized image classifier names more lesions correctly, but its preset discharge threshold misses more melanomas. Accuracy and safe decisions need separate tests.
Better diagnosis scores can conceal worse discharge decisions A specialized skin-image model beats chatbots at naming lesions — This comparison shows why choosing a model requires testing the actual decision, including which dangerous cases …
Arize's Aparna Dhinakaran explains the work behind always-on agents: trace-driven triggers, disposable sandboxes, durable state and evaluators checked against human judgment.
Kaiser Sun and colleagues compare reasoning-effort settings across eight models and four task sets. Higher effort often improves aggregate accuracy, while spending more on tasks that cheaper runs already solve. The study measures whole …
Ball's workflow centers on end-to-end access, tools that test alternatives, evidence of results, and human review concentrated on high-risk changes.
Villarreal argues that AI could deepen class domination or support collective freedom. His case for solidarity with future machines rests on disputed claims about consciousness.
Harnesses make one agent more reliable, loops start and repeat the work, and graphs coordinate it. Maddy Zhang explains when each layer earns its cost.
Bridgewater and turbopuffer explain why document permissions must shape the search itself, balancing relevant results with fast access checks.
A worked example shows how stationary model weights and a busy pipeline share the work across 59 wafer-scale chips.
Coding agents make once-impractical projects affordable. Checking the result can still be the harder job.
Jake Moran demonstrates an AI editing workflow built around accurate timestamps, storyboards, brand references and feedback drawn directly on video frames.
How huge models fit across specialized chips Cerebras explains how it spreads huge models across many chips — Its worked example explains why fast answers depend on both chip memory and the number of requests sharing it. The October 22 …
Reading a tensor value into Python can split compiled model code and force the processor to wait. IBM explains the two separate costs.
Browserbase separates reliable cloud browsers from adaptive page control, with receipt collection, vendor payments and business-verification examples.
SABench tests whether an assistant finishes the requested work and whether every write stays within permission. Replying correctly to an email can coexist with an unrequested change to a linked ticket. The study uses 201 synthetic office …