claude code's built-in context tools are good. really good.
but they're per-session, per-project. they don't compound across your whole practice.
tomorrow's newsletter is about the platform i'm building on top to fix that.
jkudish.com/newsletter
most developers write a CLAUDE.md, import a few skills, and stop there.
i didn't stop there. wrote about what i built on top in tomorrow's newsletter.
subscribe at jkudish.com/newsletter
i stopped thinking of AI as a coding assistant. it's a full implementation partner now.
i describe the problem. it asks questions. i approve the plan. it writes the code, tests, opens the PR.
my job is the decisions.
treat your CLAUDE.md as a table of contents, not an encyclopedia.
short entry point with pointers to deeper docs. architecture in one file. conventions in another.
agents navigate better with a map than a manual.
some teams are still debating whether to adopt AI. early ones are experimenting with ways to get reorganized around it.
what i'm seeing as successful patterns:
simple tasks go straight to agents. manageable tasks get a few feedback cycles. complex tasks stay with engineers.
what does your AI coding setup look like right now?
single agent sessions? parallel worktrees? custom commands? something else entirely?
curious how people are structuring their workflows beyond "open chat, type prompt."
the best way to make AI agents reliable isn't better prompts. it's fewer choices.
test suites, linters, type systems, architectural constraints. every guardrail you add removes a degree of freedom where the agent could fail.
agents don't need more intelligence. they need less room to be wrong.
every friday: run a cleanup agent across my repos. scans for stale docs, dead code, outdated patterns from the week. opens fix-up PRs automatically.
agents replicate what exists, including the bad parts. regular garbage collection keeps drift from compounding.
I'm hiring a Video Editor & Producer to work with me at Builder Methods. Know someone? You?
Looking for a creative partner to take this to the next level — storytelling, custom visuals, motion graphics. I value taste and creator chops above tactics.
buildermethods.com/jobs/video-e...
These look awesome 🤩
implementation cost is going to zero. so what's left?
vision. taste. judgment.
the cognitive demands didn't shrink. they concentrated.
about 30% of my dev time goes to meta-work now. custom commands, agents, feedback loops, memory systems.
not shipping features. building the system that ships features.
every piece compounds into the next. and eventually, i want other builders to be able to use it too. more on that soon.
the pragmatic engineer surveyed 900+ engineers. 56% now do 70%+ of their work with AI. claude code went from zero to the #1 AI coding tool in 8 months.
this isn't early adoption anymore.
also who are the other 44% and what's the 30%? am i weird for being at like 99%?
SaaS isn't dying. but it's no longer the default answer to every business problem.
Looks like a growing number of businesses are hiring builders to create internal tools instead of buying another subscription.
If I can be of help, reach out!
last week i shipped across 6 projects in ~35 hours. not because i worked harder. because AI handled most of the execution.
the leverage is real. but i'm still the bottleneck for every prompt.
writing about that tension in this week's newsletter.
subscribe: jkudish.com/newsletter
built a /deep-review command: fans out code review to 4 AI models in parallel. if 2+ flag the same issue, severity gets bumped. auto-fixes critical and high findings before the PR exists.
built on @aarondfrancis's counselors package.
what's the one thing in your AI coding setup that compounds the most?
for me it's context. CLAUDE.md files, auto-memory, project-specific configs. every session leaves behind knowledge that the next session builds on. the AI gets better at my codebase without me doing anything extra.
the more i automate my dev workflow, the more my job looks like product management.
define the problem. clarify requirements. approve the plan. review the output.
the engineering skills didn't become less valuable. they just moved from typing code to systems design.
claude code PostToolUse hooks are underrated IMO.
i added a 17-line bash script that detects gh pr create and automatically starts the code review loop. no manual step between "PR created" and "reviewer comments handled."
code.claude.com/docs/en/hooks
do you want AI to cite your brand?
own the definition.
find the specific terms in your niche that don't have clean, authoritative explanations anywhere. write the best one. structure it so AI can extract it cleanly.
I call this the "dictionary definition heist."
truths have a lifecycle. they can be superseded, contradicted, or archived.
three layers to my agentic memory system:
1. raw transcripts. every conversation stored as source of truth.
2. compressed observations. what the agent noticed and extracted.
3. normalized truths. facts with confidence scores and validity windows.
it actively resolves conflicts and automatically resolves contradictory memories.
i'm writing about it in the next issue of my newsletter which I'll send tomorrow
most AI conversations start from zero. you open a new chat and the AI has no idea what you worked on yesterday.
I built a memory system so mine doesn't work that way anymore.
three layers: raw transcripts, compressed observations, normalized truths with confidence scores.
the real find was Apache AGE; a PostgreSQL extension that adds Cypher graph queries. graph traversal on your existing database. no new moving parts.
PostgreSQL can handle a lot more than people give it credit for 😅
will share more about what i'm building soon.
i did evaluate the alternatives. Neo4j has stale Laravel packages. FalkorDB has no PHP client. SurrealDB would need a full Eloquent driver rewrite.
instead i ended up building in Laravel with Eloquent and PostgreSQL.
pgvector handles embeddings. regular tables handle the entity graph. Laravel AI SDK handles extraction. everything runs inside the same database my app already uses. no new services. no new infrastructure to monitor.
i've been experimenting with different AI memory systems. i wanted something that can store observations, connect entities, and retrieve context across sessions.
my first instinct was to reach for a hosted vector database. that's what everyone recommends.
🧵
between IAPI for interactivity and the Abilities API for AI, WordPress in 2026 is not the WordPress people dismiss. it's evolving fast and the AI integration story is stronger than most people realize, myself included.
