Coding-Agents

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.

Sonar’s Gitar workshop checks three ticketing-app changes: a refund bug outside the diff, an all-in pricing rule, and a failing check-in test.

Sonar CEO Tariq Shaukat argues for explicit intent, guidance, independent verification, and evidence as coding agents outpace human review.

David Abitbol reports Revolut’s experience serving 2,000 engineers with GLM models: lower token bills, faster delivery, and substantial GPU-serving work.

Temporal’s Shy Ruparel separates agent adoption from production reliability, then shows how recorded workflow history can preserve work across worker crashes.

Qodo’s Cole Gannaway explains cross-repository review, independent review agents, and turning recurring review comments into shared rules.

Loading pages can invalidate a correct browser plan; pixel checks and outcome checks solve different parts of the problem.

Cole Medin audits context, connections, capabilities, and cadence: small core files, restricted tools, reusable skills, regression tasks, and trusted schedules.

Addy Osmani and Manish Kapur discuss first-pass agent review, browser evidence, layered checks, and the human ownership behind larger code-change volumes.

A browser script appears to turn model safety filters against legitimate code inspection, with different results across models.

Nate Herk turns reference videos into editing skills, stages a simulated advisory debate, and automates Instagram production while retaining human feedback.

Sonar's Hunter Agent maps a repository and challenges logic-bug findings. A prepared Python demo shows 46 issues, without a detection-accuracy benchmark.

Kamil Potrec explains agent identities, delegated permissions and credential gateways, including the network controls and operational costs they require.

Written priorities redirect coding assistants more reliably than another conflicting code example in controlled repository experiments.

Coding agents can outpace your code reading. Quiroga argues for a system map and targeted investigation; HN adds review boundaries and an unresolved training problem.

Fuad Ali explains Alex's production design: harness-owned plans, explicit completion, file-backed results, small auditable memory and regression tests drawn from real sessions.

Mikyo King explains Phoenix's code-mode design: discover tools, compose them in code, and return smaller results, while handling secrets, permissions and failed executions.

Cheaper implementation expanded one engineering leader's workload. The HN discussion adds boundaries, counterexamples and a question about who gets the time savings.

Poisoned training text can leave a hidden command trigger after clean safety training; the headline rate counts 32 attempts.

A Python library gates actions by conversation state; its airline tests also expose how outcome scores miss skipped consent.

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 …

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.

Runlayer's Aiden Sochowski connects agent session logs to narrower permissions, prompt-injection defenses, broken-tool detection and spending controls.

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.

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.

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.

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.

Arize's Aparna Dhinakaran explains the work behind always-on agents: trace-driven triggers, disposable sandboxes, durable state and evaluators checked against human judgment.

Ball's workflow centers on end-to-end access, tools that test alternatives, evidence of results, and human review concentrated on high-risk changes.

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.

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.

Browserbase separates reliable cloud browsers from adaptive page control, with receipt collection, vendor payments and business-verification examples.

Laurie Voss argues that faster coding moves the bottleneck toward defining what users need. His product-engineer forecast also demands a new apprenticeship for junior developers.

Sofia Jakovcevic builds a regression-testing loop from production traces. Keep separate scores for safety and task completion, and protect workflows that already work.

Andrey Rybka maps agent incidents to scoped identities, checked tool calls, tenant isolation and outbound controls that include DNS.