Nicolas Martignole (Le Touilleur Express), co-written with **GLM-5.2** and **MiniMax-M3**, publishes **« The Token Manifesto »**: a pastiche of the **Manifeste Agile** (2001) transposed to the LLM era, where the unit of value is no longer the engineer-hour but the **token**. Four values: *short system prompts over clever system prompts*, *one clear example over three paragraphs of explanation*, *iterating in small steps over dumping the whole spec at once*, *outputting in a defined format over letting the model freestyle*. Twelve principles subvert those of Agile one by one — "simplicity, the art of maximizing the amount of work **not done by the model**," "self-organizing teams that spot repetition and document it once," "regular reflection **before the monthly bill arrives**." Beneath the humor ("staring at a usage bar nervously") lies a serious thesis: the real economic constraint of AI-assisted dev is no longer velocity but the **token budget** and the **context-window economy**. Two punchlines close the text: **« You don't have a prompt problem. You have a context-window problem. »** and **« Everyone's a prompt engineer until they run out of monthly quota. »** Worth noting, the meta wink: a manifesto on token frugality co-written *with* models.
Short note from Simon Willison (weblog) relaying two tips heard during a *Fireside Chat* at AIE with Cat Wu and Thariq Shihipar (Claude Code team): **let the model (Fable, and to some extent Opus) exercise its own judgment rather than dictating rules to it** — illustrated with the decision of whether to write tests. Second tip, from Jesse Vincent: to **save precious Fable tokens** (ahead of an imminent price increase), ask Fable to **delegate small tasks to less powerful models**, letting it judge which one. Willison shows the exact prompt used (« *use your judgement to decide an appropriate lower power model and run that in a subagent* ») and the **memory file** that Claude Code wrote in response. Domain: prompt engineering, coding agents, token economics, multi-model orchestration.
#Model judgment#delegation to subagents#model override
Long-form essay by **Shubham Saboo** (X/Twitter) advancing a thesis on the Product Manager role in the age of agents: the next key skill is **not prompt engineering** but **Loop Engineering** — designing a *system that improves with every run* rather than writing the perfect prompt every time. A **loop** is a repeated cycle: change what shapes the agent's behavior → run it → evaluate the output → keep the change if quality rises, revert otherwise → **compound the learning** so the next version starts ahead. For a PM, the entry point is not code but the **durable artifacts** that encode their judgment: PRD-review skill, customer-call *summarizer*, evaluation rubric, launch checklist, research workflow, `CLAUDE.md`, prompt template, prioritization framework. Because they are reused, these artifacts **compound in both directions** — and **drift** silently (a CLAUDE.md that keeps growing, a checklist that gets ignored…): the model has not regressed, the artifacts have drifted unwatched. A loop has **5 parts**: trigger, action, **proof**, memory, **stop condition** (the most critical). **Evals** become PM work (testing the artifact against known examples: 3 good / 3 bad PRDs, 5 understood calls, 2 past launches). **Memory** lives on **GitHub** (the repo becomes "product memory": commits, diffs, eval results, decision log, rollback). Recommended first loop: a **weekly product signal loop** (every Friday). Taste remains central — but it now needs **proof**. Cites Boris (creator of Claude Code): "he no longer writes prompts, he writes loops."
Official FinOps Foundation guide to AI: token economics, KPIs, caching, prompt optimization, model selection, and extension of the FinOps Framework's 14 capabilities to GenAI services - FinOps Foundation
#FinOps Foundation#token economics#cost per token
FinOps Foundation — groupe de travail (Brent Eubanks/Wayfair, James Barney/MetLife, Eric Lam/Google, Adam Richter/AWS, Rahul Kalva/Wells Fargo, JJ Sharma/KPMG, Karl Hayberg/EY, et al.)
Wharton study (Generative AI Labs): expert personas don't improve LLM factual accuracy - GPQA Diamond and MMLU-Pro benchmarks - SSRN
#AI prompting#personas#LLM accuracy
Savir Basil · Ina Shapiro · Dan Shapiro · Ethan Mollick · Lilach Mollick · Lennart Meincke (Generative AI Labs, The Wharton School, University of Pennsylvania)