PathruleAppunti sul contesto
per programmare con l'IA.
Appunti sul campo su assistenti IA per la programmazione, memoria del team, context engineering, costo dei token e sicurezza, con pattern pratici per costruire workflow coerenti con gli agenti.
- Ingegneria
Running the model locally was the easy partSolo in inglese
Pathrule runs a 4B model on your machine to understand your team's knowledge. Getting it to run was a weekend. Getting its output safe to act on took fifty numbered experiments, a frozen evaluation with veto power, and a deterministic compiler that has the final say.
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Introducing Pathrule IntelligenceSolo in inglese
A local intelligence layer that learns how your workspace works, how you work, and carries that knowledge across coding agents. Why memory alone is not enough, why retrieval alone is not enough, and what we built instead.
- Ingegneria
Persistent Memory for AI Coding Agents: Six DecisionsSolo in inglese
Persistent memory for a coding agent is not one product choice. It is six: instruction file or layer, what a memory attaches to, how it is found, where it lives, when it arrives, and how you measure whether any of it worked.
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Memory MCP Servers for AI Coding Agents: What Each One StoresSolo in inglese
The protocol's reference server, Mem0, Zep through Graphiti, Supermemory, Basic Memory and what happened to OpenMemory. What each one calls a memory, where it runs, how to wire it into Claude Code, Codex, Cursor and OpenCode, and the one thing MCP does not settle.
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AI Agent Memory: What It Is and How It WorksSolo in inglese
AI agent memory is the knowledge an agent can use in a session it did not learn it in. Here is the write path, the read path, the four kinds of memory and the blind spot every retrieval-only system has.
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Best AI Agent Memory Tools for Coding in 2026Solo in inglese
Most memory tools are not competing with each other. They store different units for different jobs, and picking the wrong category gives you a tool that works perfectly and still does not help.
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How to Give Codex Persistent Project MemorySolo in inglese
Codex supports local memories as well as AGENTS.md. Here is what persists, what teams can share, and where a cross-agent knowledge layer helps.
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Vector RAG vs Path-Scoped Context for AI CodingSolo in inglese
RAG is the right tool for a corpus you search. A codebase is not a corpus you search; it is a tree you are standing somewhere in, and the position you are standing in already tells you what matters.
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How Do You Know the Fix Actually Worked?Solo in inglese
Merging is a claim, not evidence. The runtime that reported the problem is the only thing that can say whether it stopped happening, and the honest answer has more than two states.
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Pathrule vs Supermemory: A Memory API Answers When AskedSolo in inglese
Supermemory is a good memory API and Pathrule is not competing to be one. The difference is delivery: retrieval waits for an agent to think of asking, and path-scoped context arrives before the first tool call.
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What a Pathrule Pattern Is, and Why It Is Not a SkillSolo in inglese
A pattern is not a single skill or rule. It is a small, opinionated bundle of memories, rules, and skills for one topic, each piece pre-scoped to the path it belongs to. Here is what that buys you and why the unit is bigger than a file.
- Sicurezza
Secure-by-Default Scaffolding for AI Coding AgentsSolo in inglese
A coding agent writes the first draft now, and the easy default is almost always the insecure one. Here is how a security pattern puts the secure default in front of the agent before it writes a single line.
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One Import Token, Scoped Across Your TreeSolo in inglese
Sharing conventions usually means pasting a long file at the root. A pattern is imported with one token that expands into scoped pieces, each placed at the path it applies to. Here is why that difference matters.
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Why a Context Layer Should Have an Open CoreSolo in inglese
A local knowledge store, readable integrations and an honest source boundary. What Pathrule keeps open, and where the proprietary Intelligence Engine and Cloud begin.
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Where Your AI Context Layer Should Be Allowed to RunSolo in inglese
For regulated and security-reviewed teams, the hard question about a context layer is not only what it stores. It is where it runs and who controls the data. Here is how to think about that line.
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Context Engineering vs Prompt Engineering: What Actually ChangedSolo in inglese
Prompt engineering tunes the question. Context engineering decides what the model sees before it answers. Here is the difference, why it matters for coding agents, and how teams put it into practice.
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What Is the Model Context Protocol (MCP) and Why Teams Use ItSolo in inglese
MCP is the open standard that lets AI assistants talk to external tools and data sources. Here is what it is, how clients and servers fit together, and where a team context layer plugs in.
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Managing AI Coding Context in a MonorepoSolo in inglese
A monorepo holds many projects under one root, which is exactly where a single AI instruction file breaks. Here is how to scope context per package so the assistant gets the right rules for the folder it is in.
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GitHub Copilot Custom Instructions: What They Cover and Where They StopSolo in inglese
What Copilot’s repository and path-specific instructions handle, and where a shared knowledge layer adds versioned authoring and cross-agent delivery.
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Why AI Coding Assistants Forget Your Codebase Between SessionsSolo in inglese
AI coding assistants are stateless. Each new session starts from zero. Here is what actually persists, what does not, and how teams keep context across sessions.
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How to Share Claude Code Memory Across a TeamSolo in inglese
Claude Code stores memory per project and per user by default. Here is how teams actually share that knowledge: what CLAUDE.md covers, where it stops, and what fills the gap.
- Sicurezza
Claude Code Hooks vs CLAUDE.md: Where Enforcement LivesSolo in inglese
CLAUDE.md is advisory. Hooks are deterministic. Here is the practical split, why teams should use both, and where a path-scoped knowledge layer fits.
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Why Claude Code Skills Do Not ActivateSolo in inglese
Skills look like they install cleanly and then quietly do nothing. Here is why SKILL.md often fails to activate and what a working delivery path looks like.
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Why Cursor Rules Get Silently IgnoredSolo in inglese
Cursor rules look loaded and then quietly do nothing. Here is what breaks, what to check first, and how a path-scoped layer keeps rules visible at the moment they matter.
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Why Claude Code Ignores CLAUDE.mdSolo in inglese
CLAUDE.md is guidance, not enforcement. Learn why AI coding assistants skip long instruction files and how hooks plus path-scoped rules close the gap.
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AGENTS.md vs Pathrule: When Each One Makes SenseSolo in inglese
AGENTS.md gives AI coding agents one predictable file. Pathrule keeps it short by routing the right team knowledge slice per session.
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Context Is a Team Interface, Not a Prompt FileSolo in inglese
A practical argument for treating AI context as shared team infrastructure, not another prompt file to maintain by hand.
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Why AI Coding Assistants Need Scoped RulesSolo in inglese
Learn why AI coding rules work best when they are attached to the files and folders where they apply, reducing noise while preserving precise constraints.
- Costo dei token
The Cost of Rediscovering Your Own CodebaseSolo in inglese
AI coding sessions often waste time and tokens rebuilding knowledge the team already has. Scoped context changes the first move.
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From Tribal Knowledge to Reviewable MemorySolo in inglese
Learn how teams turn scattered project knowledge into visible, scoped and reviewable memory that AI coding agents can reuse across sessions and tools.
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A Practical Model for Safer AI Coding WorkflowsSolo in inglese
Build safer AI coding workflows with explicit boundaries, scoped rules, reviewable memory, source-code privacy and clear human approval points for teams.
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Why Global AI Instructions Break Down at Team ScaleSolo in inglese
Learn why global AI instruction files lose signal as teams add more paths and exceptions, and how scoped context keeps local constraints precise for teams.
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Just-in-Time Context for AI Coding AgentsSolo in inglese
AI coding agents work better when relevant team knowledge arrives before the first useful action, not after a search loop.
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Memory, Rules, and Skills: Three Kinds of Team KnowledgeSolo in inglese
Pathrule separates team knowledge into memories, rules, and skills so AI coding assistants receive the right kind of context.
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How Stale Team Knowledge Creates AI MistakesSolo in inglese
Learn how stale team knowledge creates repeat AI coding mistakes, then use review, repair and retirement workflows to keep shared context trustworthy.
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One Knowledge Layer Across AI Coding AgentsSolo in inglese
Teams should not rewrite the same guidance for every AI coding agent. Pathrule gives shared product knowledge one home across supported coding agents.
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Design Rules Should Travel With the Work, Not the ToolSolo in inglese
Keep AI-generated interfaces consistent by attaching product and design rules to the work itself, so the same constraints follow every coding tool you use.
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Team Memory Should Not Change When AI Tools DoSolo in inglese
Keep team memory stable while engineers switch AI coding tools, so shared decisions and conventions remain portable across agents, sessions and editors.
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Audit Trails for AI Coding: What Changed, Who, and WhySolo in inglese
An audit trail for AI coding shows who changed a rule, memory, or skill, when, and why. Here is why the context your assistants act on deserves the same review and history as code.
- Sicurezza
Pathrule Stores Team Knowledge, Not Source CodeSolo in inglese
Pathrule is built around a narrow privacy boundary: store the knowledge teams choose to write down, not their repository source.
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Small Teams Should Build a Context Layer EarlySolo in inglese
Small teams feel AI context problems early. Capturing knowledge before the team grows keeps onboarding, review, and AI work calmer.
- Costo dei token
More Context Is Not Always Better for AI AssistantsSolo in inglese
AI assistants do not need every fact at once. They need the right context, at the right depth, for the task in front of them.
- Casi di studio
A Real History Test: 30x-50x Lower Context CostSolo in inglese
A real Pathrule history query showed how scoped prior work can avoid broad local search and reduce tool-output token cost.