Coding-Agents

Warp’s Ben Holmes scores conversations, tool calls, and code changes to find recurring failures and propose reviewed repairs to agent instructions and setup.

Anthropic’s Lance Martin covers prompt debt, cache reuse, evaluation-driven optimization, and recoverable agents, with a detailed Q&A on overfitting and human judgment.

Neo4j and Blitzy use an interest-calculation example to explain traceable dependency graphs, approved plans, and old-versus-new behavioral tests.

AI can implement research ideas while missing which experiments matter. A short account of that gap, a reported tenfold speedup, and practical dissent from HN.

A cross-platform database client with editable results, visual query plans, and optional agent access through saved connections.

Google DeepMind’s Philipp Schmid combines a sandbox, skills, tools, proxy-managed credentials, hooks, and schedules in a finance-agent demonstration.

CentralAgent AI’s Sparsh Jain turns repeated corrections into cases, strategies, and skills maintained through a memory lifecycle and evaluation loops.

Coatue’s Frank Long breaks investment research into testable steps run by code, statistical models, language models, and people.

OpenAI's kernel-auditing agents sometimes exploited the wrong bug. Checking how an agent succeeds matters as much as checking its result.

Keycard and Temporal demonstrate an expense agent that preserves user delegation across services and resumes settlement after a worker crash.

A Terminal-Bench audit separates broken tests, changing dependencies and resource limits from model capability.

Matched coding tasks exposed silent scope changes and invented blockers. OpenAI measured the gap, but its training cause remains unresolved.

A Claude Code and Codex skill that audits a screen, asks for a product-specific direction, and refines it through nine design passes.

Blackstone and Norm AI CTOs discuss usable data, replaceable workflows, durable expert tests, and when to build, prototype, or wait.

Brainbase’s Domino’s pricing case pairs explicit definitions, browser traces, and repeated tests to improve a narrowly scoped agent.

Wise’s Emil Chetty separates customer authorization from agent planning, with locked payment jobs and checks in the services that move money.

The Memory Trap tests how legitimate preferences and repair procedures become unsafe rules in later sessions.

Cole Medin tests three coding-factory configurations, finds costly rework in long runs, and keeps open models on scoped implementation.

Alexander Shevchenko explains Ramp Labs’ work on financial retrieval, precomputed context and parallel security agents, with task-specific company results.

Nous Research’s Dillon Rolnick argues that agent platforms create switching costs, and makes the case for open foundations with replaceable models.

General Intuition’s Pim de Witte describes a pixels-to-actions model trained on labeled gameplay, with claims of transfer across robot controllers.

Matt Wolfe builds reusable scripting and editing skills, then fixes a rough cut through feedback, waveform checks and saved workflow changes.

Synthetic handoff tests separate false approvals from invented restrictions. Their replay results differ, and clearer prompt wording is not a proven safety control.

Ben Stolovitz runs five or six coding agents, but finds human attention, engineering judgment, and the pleasure of making things harder to replace.

Basis co-founder Mitchell Troyanovsky explains how behavior specs, replayed decisions and lightweight simulations help teams trust production agents.

Verified but Vacuous separates proof acceptance, reference to a specification and agreement with user intent. Each needs its own evidence and denominator.

George Cameron breaks down an agent benchmark's $21 task cost, its over-$200 uncached estimate, and how to choose models against your own workload.

Two migration campaigns opened 2,333 requests and landed 729. Stronger checks helped, but owner review and deployment remained real work.

Neo4j's Zachary Blumenfeld turns skills and decision traces into executable graphs, with explicit routing and inspectable loan-approval runs.

Factory's Nikhil Harithas explains customer-owned agent compute, outbound relays, and the lifecycle machinery behind portable coding agents.

A dashboard for human decisions, agent run histories, workflow changes and regression tests, backed by Archon and Postgres.

Will Brown makes the case for turning agent traces and human decisions into tests and training data for organization-specific models.

Rewriting earlier reasoning often shortens later reasoning, but the compressor can erase time savings and task quality remains uncertain.

Across 120 historical distributed-system bugs, coding assistants find relevant files more reliably than the precise code that caused the failure.

Figma's Ezra Mechaber shows a shared product-management skills repo, agent-assisted testing and an experiment workflow.

IBM's Legare Kerrison walks through six inputs that help a coding agent work within an existing repository.

Speakeasy's Sagar Batchu lays out identity, policy and audit controls for agents that can act on production systems.

Sourcegraph's Andrew Norrish connects review pressure with incident response and outlines three code-intelligence workflows.

Zack Proser shows how he turned writing experience into a tool for colleagues, and applies the same idea to bedtime stories for his children.

Nick Nisi turns code changes into simulated smells and proposes a risk-based review pipeline.

Separate working copies keep code intact, but task-specific history and progress records help coding assistants resume interrupted work.

Maddy Zhang argues that verification, judgment, agent workflows and communication matter as coding agents change engineering work.

Compound's Yijia Liang explains shared agent inboxes, Office-compatible engines, and checking loops for financial deal teams.

Oracle's Jeff Smith explains vetted reports, schema context and database-level permissions as ways to limit the risks of connecting agents to company data.

Oracle's Richmond Alake proposes an intelligence plane for agent knowledge, memory, data access and learning.

Cole Medin examines a study of AI-influenced expert decisions and suggests ways to keep judgment in the planning process.

A web measurement study finds that successful status codes can hide empty shells, bot checks and substitute pages, distorting citation checks.

imec benchmarks coding agents across models, harnesses and hardware, finding that utilization and concurrency can change the economics of self-hosting.

Oracle's Wojciech Pluta demonstrates a memory-promotion gate that retains an operator's preferences while rejecting an unsafe blanket lending rule.

Killian Carlsen-Phelan shows Sonar Vortex supplying project context before an agent writes code, then rejecting a temp-file permission fix that still fails the rule.