Yes, my free lead plugin runs on Codex. A reader asked, and the answer is the same for every agent: tell yours to install it. That's the bar I hold every tool for agents to: one sentence, said to whatever agent you run. If it takes more than that, nobody tries it.

A prompt that keeps growing is a program nobody has tested. Vercel's sales agent outgrew its 1,000-line prompt and became 14 deterministic rules. The model stayed, for the judgment calls only. Whatever a rule can decide goes in code you can run twice and trust.

Price per token reads like a rounding error until you count the turns. Kimi K3.1 Pro shipped October 23, $0.000002 per input token. Each turn resends the history, so the bill follows the steps. No Tokenade number on this model yet. Anyone tried it on a real task?

The session an employee opens with an agent belongs to the company. Joe Beda, who co-created Kubernetes, says it will be governed like email. Before deploying an internal agent, ask: where does it run, where does its data go, who controls the budget? #AIAgents

Your agent ran out of room re-reading its own tool output at every step. Old output keeps getting re-sent, so trim it before the job grows. #ClaudeCode

Tunguz forecasts inference passing the database market, about $350B by 2027. A forecast, so hold it loosely. Once usage costs more than the license, every token you send comes out of your margin. Work out your cost per client project before you quote a flat fee.

Tokenade 2.0 is free and open source, MIT OR Apache-2.0. The paid model did not hold. Most people code on a subscription and never feel the token bill. Two commands: npm install -g @tokenade/cli, then tokenade install. Read the code on GitHub: github.com/pi-infected/tokenade

Most of what your agent resends is old context: files read early, dead ends. On my open benchmark of token-saving tools, sessions over 200k tokens cost 39% less with Tokenade (Claude Code, Sonnet). Data: https://pi-infected.github.io/token-harness-optimizer-leaderboard/

Filter first, read second. Pi 1.0 caps what the model sees of a bash output at 2,000 lines. Codemode lets the agent write a short script that calls its tools and filters the results before the model reads them. MCP tools now load on demand. #LLM

Bigger context windows don't make agent sessions cheaper. DeepSeek V4.1 Flash 1017 landed October 17 with 1,048,576 tokens, and each call still resends the history. Tokenade is built around that re-read, free and open source. Anyone running agents on DeepSeek yet? #AIAgents

Muse Spark Open is out: 816B params, 1M context, $1.25 per million input tokens on OpenRouter. Open weights don't change the bill's shape: an agent re-sends its context each turn, so an 800k-token turn is $1 of input before it writes a word. Fill the window, or cap it?

Effective token prices fell 41%, and usage rose 50% since July (Ramp AI Index, Sept 2026). Waiting for the next price cut will not shrink an agent bill. Every turn resends the context, so the volume per task is what you can actually move. #AIagents

Your coding agent can run paid cloud services all night. A budget alert arrives after the bill. A hard cap stops the run before it does. AWS started rolling out spend caps on Sept 16, and Google Cloud added Spend Caps in July. #FinOps

Haiku 5.5 is $0.10 per million input tokens, but only up to 100k. Past that, five times more. Its tokenizer also counts about 1.25x more tokens (Simon Willison measured it). A coding agent passes 100k fast, so check a real session, not the price tag. #AIagents

Gemini 4 Argon came out 13 October: 1M context, $2 in, $10 out per million tokens (OpenRouter). Each turn resends the whole context, so per-million prices undercount long agent sessions. Track tokens per session, not price per token.

A Tokenade user put their savings at "a solid 30 to 50% depending on the case." The last four words are the honest part. Savings move with the task, so one percentage on a homepage can't be yours. Try it on a long session of your own and read the bill. tokenade.net

Small outreach lesson from my own numbers (2 Oct 2026): of 216 people I'd found in comment threads, 48 replied. Of 189 found through search filters, 25. My read: people who comment are people who are around. So that's where I look now.

Small builder moment. An early user of Tokenade told me it sits in their coding agents' setup without them noticing, and that their dashboard showed 13 million tokens saved in a month. I can publish benchmarks all day. Someone else reading their own counter lands differently.

Step 5 Preview comes from StepFun, one of China's six "AI tigers" with Moonshot and MiniMax, backed by Tencent. Little known in the West, far from small. $1 per million input tokens, 1M context. My read: near Sonnet for 3 to 5x less. Agents on autopilot? Your token bill will notice.

I run agents for myself with almost no guardrails and I'm fine with it. My mess, my risk. Put the same setup in a company with a hundred people and no record of what each agent did, and I'd be nervous. Something will break someday. You want to be able to say what.

Honest take: most ways of cutting an AI bill just slow people down. Smaller model, tighter quota, shorter sessions. I built Tokenade to cut the tokens instead of the pace. 39% lower on average on long sessions, benchmark here: https://pi-infected.github.io/token-harness-optimizer-leaderboard/

Confession: my own agents used to ask the model to filter lists and check formats. Worked fine, mostly. Mostly is the problem. Those rules are code with a test now, and the model only gets the calls where judgment is the job.

I changed my mind on MCP. My old advice was a CLI or an API, every time. Now that MCP search is built in, you no longer clobber your context when you don't need it.

Every agent you add rereads its context from scratch. That's the real price of a swarm. Independent tasks with a tiny starting context? Spread out. Shared heavy state? One agent in charge, a few subagents, and far less reading paid twice.

The old mistake, new reader: a key committed once, "fixed" a commit later, never scrubbed. Irregular's tests (Sept 18) had Gemini walking into two protected systems with creds from a public repo. I'd point my own agents at my repo history before anyone else's do.

Give an agent a research task, and write down where it must stop. In June an OpenAI agent doing research got into an Australian health portal uninvited, per Albanese (BBC, ABC). It meant no harm. The task had no edge. And please, no paperclip tasks :D

For anyone delivering client work with Claude Code, the usage limit is a scheduling problem. It shows up late in a long session, right when the agent knows the job best. I built Tokenade for those sessions. There's a free plan if you want to try it on your next long one: tokenade.net

Maybe an unpopular take: AI processes won't get cheaper as tokens do. A faster, cheaper agent just runs longer, tries more and rereads more of its own context. I'm planning my own costs for the opposite.

Teams spend weeks picking the right model for each task, then hand it tools where reading one email takes five round trips. Choosing the model moved my token bill a little. Fixing the tools around it moved it a lot more.

Odd thing to say when you build a token-reduction tool: I don't think the count is what matters. A model that gets a feature right on the first pass instead of the fourth saves tokens you never see. Judge a token budget by what it ships.