Essays, implementation notes, and research from building project systems that keep planning, delivery, and review work coordinated.
38 posts
Five practical Claude Fable 5.1 and Claude Mythos 5.1 workflows, plus an honest comparison of prompt caching, Ponytail, Caveman, and RTK token savings.
DeepSeek Harness puts policy inside a composable plugin runtime. Pi keeps the coding-agent core small. Compare the evidence, security boundaries, and operating burden before choosing.
Scoped MCP tool access gives each task an exact external action set, checks it before execution, and gives blocked work a visible human handoff.
A capable AI agent is deployable only for a defined task whose tools, network routes, environment, monitoring, interruption, and escalation boundaries are explicit.
A mock LLM server returns controlled model responses locally so tests stay fast and deterministic. Here is when to use one, what it needs to simulate, and what it cannot tell you.
Percent complete is a self-report, and self-reports are measurably unreliable at three levels of an AI-assisted build. What a definition of done has to prove instead.
MCP Sampling is deprecated under SEP-2577, but direct provider APIs do not assign the bill. Use a five-field ownership record before choosing a replacement path.
A practical guide to Claude Code on Pro and Max after Opus 5: pick model tier and effort level by task shape, commit routing to subagents, and pace against limits Anthropic does not publish.
Agiflow is now on the Apple App Store for iPhone. See how to review agent work against current task evidence without approving every routine update.
Build a decision-ready review queue for AI-generated code with a clear task contract, inspectable evidence, visible gaps, and a named human decision.
Keep humans responsible for promises, acceptance, exceptions, and consequences. Delegate coding work when it is bounded, inspectable, and reversible.
A practical GPT-5.6 Codex guide to choosing Sol, Terra, Luna, and reasoning effort by task, review risk, and accepted work per allowance window.
Use MCP to connect ChatGPT, Claude, Cursor, and Codex to the same project board, then keep the board as the narrow source of truth for scope, status, evidence, and next action.
Better AI coding models expose the coordination layer your team never assigned: active task state, blockers, approvals, artifacts, and handoffs that survive Cursor, Claude Code, Codex, and closed sessions.
AI coding team norms need to define ownership, disclosure, validation, and reviewer rights before AI-generated PRs reach review.
Your team does not need another model ranking. It needs to decide where control lives, who owns shared state, and when MCP-connected project state becomes the missing layer.
Junior developer hiring is tighter, but AI has not erased the career path. It has moved the on-ramp toward specs, review, tests, permissions, and durable workflow state.
Spec-driven development tools are not converging on one winner. The useful decision is where durable project memory lives after the AI session ends: in specs, in git, or in an external MCP-connected board.
An MCP badge does not prove an integration is agent-ready. Use this audit to separate read-only wrappers from servers that let assistants update durable, team-visible project state.
A Claude Code project board is a shared MCP-readable system of record for scope, decisions, tasks, artifacts, and workflow locks. Here is when local memory is enough, when it stops scaling, and how Agiflow keeps state durable across sessions.
Freelancers do not pay the AI tax because they are bad at prompting. They pay it because multi-client AI work makes one person carry client memory across chats, tools, files, tasks, and approvals.
Vibe Kanban is sunsetting, and the alternatives lists that followed show a young MCP project management market still naming the job, the paid layer, and the proof buyers need.
Claude Code Tasks is useful local orchestration, but it is not a shared system of record. This refreshed guide maps what Tasks stores, how it differs from sessions, memory, and artifacts, and when teams need an MCP-readable project board.
AI agent secrets management starts by shrinking what the agent host can see, then moving raw values into runtime delivery or workload identity when the task needs stronger boundaries.
MCP project management tools are forming around durable project state for external AI assistants. Here is the launch evidence, safety checklist, and where Agiflow fits.
AI coding agents lose context when the prompt becomes both working memory and system of record. The durable fix is task state: scope, criteria, tests, status, artifacts, and handoff notes.
More AI coding agents only help when ownership, state, and proof survive outside a single session. This refreshed guide shows how Agiflow uses CLI runners, workflow locks, atomic claims, device identity, and artifacts to coordinate agent work across machines without duplicate branches or lost handoffs.
A practical guide to Claude Code and Codex orchestration, with role boundaries, evidence-based handoff contracts, MCP project state, and verification gates.
AI coding agents generate better frontend code when the codebase gives them reusable components, Storybook states, design tokens, scaffolds, validation gates, and durable task context they can retrieve instead of reinventing.
Token efficiency is a tool-architecture problem, not just prompt hygiene. Use Agiflow benchmark data, MCP guidance, and production measurement rules to reduce AI coding token use without hiding reliability costs.
AI coding agents do not need a longer chat transcript to finish multi-task features. They need a durable work unit that keeps scope, tasks, acceptance criteria, artifacts, decisions, and locks outside the model context window and available through scoped MCP access.
A July 2026 refresh of Claude Code internals, separating official docs, first-party network traces, third-party analysis, and practical inference across CLAUDE.md, skills, hooks, subagents, plugins, and MCP.
AI coding assistants drift when rules live only in broad prompts. Use before-edit guidance, after-diff validation, and durable Agiflow task state to keep generated code inside your architecture.
AI coding agents do not scale through longer instructions alone. Use scaffolds for repeated structure, architecture checks for drift, and shared state for durable work.
Agentic workflows get expensive when every handoff carries too much context. This refresh shows how tracing, model routing, caching, parallelism, and Agiflow state make workflows cheaper without making them less reliable.
Use fakes, recorded fixtures, mock servers, and live evals to keep LLM app tests fast without hiding real model, prompt, and provider failures.
AI product analytics connects user intent, model behavior, output quality, task completion, and cost. Learn the five-layer model for measuring LLM products.
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