How I Pick AI Subscriptions by Budget and Role
Before you compare prices, decide whether you code for work. The same budget buys a completely different stack for builders and non-builders.
Anthropic, OpenAI, Google, and other AI platform deep dives.
101 posts
Before you compare prices, decide whether you code for work. The same budget buys a completely different stack for builders and non-builders.
I keep a living Codex Vible for install, settings, App vs Web, and how it differs from ChatGPT, because the product ships updates faster than most people can relearn it.
Two structural issues in Codex defaults cut reasoning short and explode agent trees. Here is what I measured and the config.toml changes that fixed both.
Matt Pocock's method: run /context, rank tool payloads, then trim settings.json with disable flags and bare tool-name denies so unused definitions stop riding every message.
Dense pixel fonts pack ~40k characters into one Anthropic image bill. pxpipe production logs show ~59% savings, with caveats: Fable 5 only, and keep IDs as text.
When Fable 5 is available but capped, keep effort at high, reserve it for judgment, and push bulk work to Codex or Sonnet. Write the routing into CLAUDE.md first.
appintoss-kit layers AGENTS.md, guides, skills, hooks, and review agents so Codex/Claude/Cursor stop missing policy, Korean UX, design rules, and store metadata.
A ChatGPT→Codex handoff bridge, multi-account config sharing via symlinks, a menu-bar status app, and an imagegen API with provider fallback.
I shared the deck and seven solo-builder workflows from the only solo-builder session I ran at Founder Day 2026 — including the fact Codex built the slides.
A full global skills library loads junk into every session. Keep one warehouse of clones and symlink only what each repo needs.
ChatGPT-as-MCP planning, multi-account gateways, Excalidraw intent, imagegen fan-out, remote handoff, grill-me PRDs, kanban agents, and multi-tool control.
The day reinforced that solo builders can hold a room with executives — if the workflows are real and the deck is honest.
A short field note from a packed room: builders, executives, and the odd feeling of being the only solo operator on the agenda.
When knowledge for agents is formatted like a product surface, wikis stop being human-only documentation.
Encode review taste as rules and examples, then let an agent apply the same compressions a strong reviewer would.
Pick one of six structures, lock design tokens, run a 3-second test — stop getting pretty bullet dumps with no “so what.”
No IDE heroics: plan.md, voice, cmux tabs, remote control, skip-permissions discipline, research-before-plan, and resting before AI psychosis.
Blog version of a 2026-05-30 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-29 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-26 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-26 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-25 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-25 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-23 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-21 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-21 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-21 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-19 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-19 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-19 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-16 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-15 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-13 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-12 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-12 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-10 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-08 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-05 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-05 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-04 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-05-02 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-04-30 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-04-29 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-04-23 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-04-23 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-04-22 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-04-21 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-04-19 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-04-18 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-04-13 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-04-10 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-04-10 LinkedIn note — same claims, cleaned structure for reading.
Weak UI usually means weak visual language in the prompt.
Blog version of a 2026-04-09 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-04-09 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-04-08 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-04-06 LinkedIn note — same claims, cleaned structure for reading.
I edited config.toml, wrote rules in AGENTS.md, and nothing stuck. Turns out the folder structure itself was the issue, not my settings.
OpenAI shipped Codex as a Claude Code plugin on the same day Anthropic announced Computer Use. I think it's the smartest concession of 2026.
Blog version of a 2026-03-29 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-03-28 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-03-25 LinkedIn note — same claims, cleaned structure for reading.
I spent a year getting wildly inconsistent results from Claude Code and Codex. Three spec files, each with a distinct role, fixed it.
Subscribing puts you in the top 0.3%. These five configurations — agents, teams, MCP, monitoring, automation — push you into the top 0.01%.
I classified every term I kept encountering while using Claude Code and Codex daily. Five groups emerged, and they map the entire system these tools run on.
I dug into SDK type definitions and system prompts for both tools. The 29 vs 7 gap isn't about feature count. It's about two fundamentally different answers to the same question: how should an AI coding agent interact with your system?
Blog version of a 2026-03-12 LinkedIn note — same claims, cleaned structure for reading.
I reverse-engineered how Codex handles context overflow compared to Claude Code. The answer involves AES encryption, session handover patterns, and KV cache tricks.
A race condition between Auto Memory and context compaction in Claude Code v2.1.59–v2.1.61 broke prompt caching and corrupted sessions. Anthropic reset all weekly limits as compensation.
I couldn't sleep after a conversation about shipping more work publicly, so I built free-router at 3am. It pings free AI models in real-time and wires them into your coding tools with one keystroke.
Three companies updated their coding agents at the same time. The directions overlap. The real battleground isn't models; it's how fast they absorb developer workflows.
My API costs jumped 10x when the cache broke in production. The same day, Anthropic engineers explained exactly why.
From Cloudflare and Vercel's Markdown for Agents to Google's WebMCP, reading and writing are being standardized simultaneously, ushering in the Agent-Native Web era.
Korea-exclusive KakaoTalk promotion offers ChatGPT Pro at 29,000 KRW instead of $220/month, plus the new Codex-5.3-Spark delivers 1,000 tokens per second.
Peter Steinberger joining OpenAI isn't just a talent grab. It signals the dawn of AI-native messengers that could redefine how we communicate.
OpenAI's Codex team built a 1M-line codebase using only AI agents. Here are the five harness engineering principles they discovered along the way.
Blog version of a 2026-02-11 LinkedIn note — same claims, cleaned structure for reading.
Opus 4.6 Fast mode costs $150/output tokens. This isn't just pricing, it's the birth of a new economic divide where token access determines competitive advantage.
Meritech Capital's analysis of 100+ public software companies reveals a stark valuation gap between AI-executing and non-AI firms.
While the market warns of GPU overcapacity, OpenAI declares it needs even more compute. The real winner won't be whoever has the most power - it'll be whoever closes the gap between AI capability and actual user experience.
OpenAI and Google are racing to launch affordable AI plans while Chinese competitors shatter price floors. Here's why this moment is your best entry point.
Anthropic's Tariq Shihipar breaks down what it actually takes to build production-grade agents - from Bash-first tooling to file-system-driven context engineering.
Anthropic launches Cowork, an autonomous agent that reads, edits, and creates files on your local machine. Vibe coding meets vibe working.
Anthropic's Claude Opus 4.5 didn't just set new benchmarks. It proved that going all-in on text, code, and agents while competitors spread thin is the winning play.
Why $300B evaporated from SaaS stocks as ChatGPT and Claude race to become the AI app store - and what the 2008 mobile wars tell us about what comes next.
Anthropic's Claude in Excel reveals the gap between AI-augmented and AI-native - and why most startups building 'AI + X' products won't survive 2026.
Manus shared the hard-won lessons behind building production AI agents - from context rot to evaluation rethinking - in a joint presentation with LangChain.
Meta acquired Manus for $3.6 billion. The secret wasn't a bigger model - it was context engineering. Here's what most AI agents get wrong.
Meta acquired Chinese AI startup Manus for billions. This deal reveals a new reality: going global isn't a growth option - it's a survival strategy for every startup in the AI era.
A deep dive into Oh-My-OpenCode's multi-agent orchestration architecture - how programmatic context isolation, parallel execution, and evidence-based research are redefining what AI coding agents can do.
Opencode's open-source documentation doubles as an introductory guide to agent architecture. Here are the seven core concepts every developer should understand.
Blog version of a 2026-02-04 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-02-02 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-01-17 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-01-15 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-01-13 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-01-11 LinkedIn note — same claims, cleaned structure for reading.
Blog version of a 2026-01-08 LinkedIn note — same claims, cleaned structure for reading.