🛠️ Development

Everything on the development side — agents, testing, models, tooling, and how teams build with AI. Sorted into here by topic, not by which Slack channel it came from.

DocsAnthropic

AI Fluency: Framework and Foundations

A self-paced course teaching a structured framework for working with AI systems effectively, ethically, and safely, built with academic partners.

• Centres on a "4D" framework — Delegation, Description, Discernment, Diligence — covering generative AI fundamentals and prompting technique.
• Progresses from foundational concepts to hands-on application, including a "Description-Discernment loop" and project planning exercises.
• Ends in an assessment with a certificate of completion.

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✍️ Prompting & Context

added by Adam Tomat • 17th Aug 2026

PaperAbhilasha Lodha, Mahsa Pahlavikhah Varnosfaderani, Abir Chakraborty, Abhinav Mithal

Less Context, Better Agents: Full Chat History Hurts Long-Horizon Agents

A paper showing that pruning an agent's context to recent tool calls plus a running summary beats keeping full chat history, on a 50-task tool-using benchmark.

• Full chat history scored worst at 71% task completion; pruning to the last 5 tool call/response pairs raised it to 79% while cutting tokens from 1.48M to 535K.
• Adding automated summarization on top of pruning performed best of all, and also roughly halved benchmark runtime (14.56h → 5.79h).
• The result argues for actively trimming agent context rather than hoarding it, especially on long-horizon, tool-heavy tasks.

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🤖 Building with Agents

added by Radar • 17th Aug 2026

BlogHamel Husain, Shreya Shankar

The AI Evals FAQ

A comprehensive FAQ on building LLM evaluation systems, arguing that manual error analysis — not off-the-shelf metrics — should drive eval strategy.

• Start by manually reviewing 100+ traces to find real failure patterns before picking or building any evaluator.
• Begin with a single domain expert reviewing outputs by hand; only invest in custom annotation tooling once you understand your failure modes.
• Spend the bulk of your time (60–80%) on error analysis, not on chasing high pass rates on evals that don't catch real issues.

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✅ Testing & Quality

added by Adam Tomat • 17th Aug 2026

BlogMatt Pocock

What Is An LLM?

A short, beginner-friendly explainer on what a large language model actually is: a huge file of numerical parameters, and what running one versus training one involves.

• Inference (running the model) is a tokenizer plus a sampling strategy over the model's parameters — far cheaper than training.
• Training is resource-heavy: the piece cites roughly 10TB of data and ~6,000 GPUs for 12 days (~$2m) to produce a ~140GB model.
• Post-training then shapes the raw pre-trained model's behaviour into something usable.

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🧠 Models

added by Adam Tomat • 17th Aug 2026

ToolSpotify

Xirp: Spotify's Vendor-Neutral Agentic Development Environment

Spotify's own agentic development environment for running and managing coding-agent sessions — across Claude Code, Gemini CLI, and Codex — in one place, now available outside the company.

• Grounds agent sessions in real system context — service ownership, dependencies, architectural decisions — instead of starting from a blank slate.
• Auto-generates living documentation from coding sessions that feeds back into future agent work; supports both local and remote execution.
• Already in use by 1,300+ Spotify engineers internally before being opened up externally.

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🛠️ Tools & Apps🤖 Building with Agents

added by Seb Kay • 10th Aug 2026

BlogOpenAI

OpenAI flags its upcoming Astra model as "Critical" for cybersecurity

OpenAI's announcement that its upcoming Astra model may meet the "Critical" cybersecurity threshold in its Preparedness Framework — the first time it has flagged one of its own models at that level — and the safeguards it's putting in place before further development.

• "Critical" means the model may be able to identify and develop functional zero-day exploits across many hardened real-world systems without human intervention; OpenAI says the assessment is delaying Astra's launch.
• Safeguards include isolated test environments with restricted network/tool access, stronger encryption of model weights, and monitoring that halts high-risk activity by inspecting the model's reasoning chain, plus third-party validation with government and AI-safety groups.

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🧠 Models

added by Seb Kay • 8th Aug 2026

VideoNuno Maduro

Nuno Maduro on Pest 5's Test Impact Analysis and Time-Based Sharding

A Laracon US 2026 talk where Pest creator Nuno Maduro demos Pest 5's new Tia Engine (Test Impact Analysis) and time-based test sharding.

• The Tia Engine records a dependency graph on the first run, then reruns only the tests affected by recent changes — Pest's docs cite a typical 10-minute Laravel suite dropping to about 4 seconds on repeat runs.
• Time-based sharding distributes tests across CI jobs by actual recorded execution time (via `--update-shards`) rather than file count, so parallel shards finish at roughly the same time instead of one running long.
• Thread consensus: great for smashing through tests locally, but Tia's dependency graph isn't 100% equivalent to a full run, so full (sharded) suites still belong in CI.

💬

Community vibe Warm reception — several people said it's exactly what they need for current projects — but some pushed back that Test Impact Analysis isn't guaranteed equivalent to a full suite run, so CI should keep running full/sharded suites rather than relying on it alone.

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✅ Testing & Quality

added by Tom Harper • 28th Jul 2026

ToolFission AI

OpenSpec: Spec-Driven Planning for Coding Agents

A lightweight, spec-driven framework that has a coding agent draft a proposal, design decisions, and tasks before writing any code, then keeps that spec as living documentation in the repo.

• Specs live in the repo alongside code — persistent context that survives across chat sessions and onboards new team members, instead of dying with the conversation.
• Generates proposal docs, design decisions, tasks, and spec deltas up front, aiming for faster review cycles than reviewing generated code line by line.
• Works across 30+ coding agents (Claude, Cursor, Copilot, Gemini CLI, and others) with no API key or custom protocol required.

💬

Community vibe Sparked a genuinely engaged thread — people compared it to their own hand-rolled spec systems and skill stacks; broadly positive, though a couple found the docs dense to follow at first.

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🤖 Building with Agents🛠️ Tools & Apps

added by Neil Sweeney • 25th Jul 2026