№ 00 — PRAVDA RESEARCH
Deep research on agentic AI, as a membership.
Independent, sourced field research on AI automation — read it, hear it narrated, test yourself on it, and discuss it with other builders. Free pieces stay free; membership funds the deep work and unlocks the member research, skills and files.
№ 01 — FROM THE LIBRARY
- The Agent-Native Stack: What to Standardize On When AI Agents Do the Building
The failures that cost money are never the model: they are blanket-scope tokens, silent transport misconfigurations, and dashboards that measure health instead of correctness. The agent-legibility test, the blast-radius rule, and a default stack per product archetype, from 971 sources and the incidents that prove them.
- Your pipeline is lying by staying silent: the append-only ledger that catches broken publish packages
File presence is not pipeline state. My strongest article sat three-quarters unsyndicated for days, and the one package that did exist was missing its images because a bare try/except swallowed the failure. The fix is a committed append-only event ledger plus one idempotent command that replaces fourteen hand-run steps.
- How to Turn Drunk AI Agents Sober: The Harness for Coding Without Babysitting
How a solo dev runs 3-5 parallel Claude Code sessions plus an overnight lane without watching terminals or eating a five-figure bill: worktree isolation, hooks that block instead of ask, budget caps, and reviewers that never grade their own homework. Built from 967 sources, the field's real burn stories, and the machinery I hardened running a 215k-line Rust trading system solo.
- What Businesses Actually Pay For in AI Automation: Real Listings, Not Rate Cards
We ran the AI-services pricing internet through adversarial verification and almost all of it died. What survives: 1,560 real marketplace listings, audited financials, and a demand map that tells a solo engineer what to build, which vertical to own, and what the first real price is.
- How X/Twitter growth actually works in 2026: pass the AI's value prediction, then out-system everyone
X reach in 2026 is decided before anyone sees your post, by an AI value prediction. I mined 23,000 automation posts to find the levers that actually move reach, why follower count is the wrong scoreboard, and how to separate reach from revenue.
- AI video production and tools: the cinematic and factory camps, the transcript you actually own, and the local-versus-cloud rule
AI video's real bottleneck was never the prompt, it's the system. Two camps, cinematic craft and factory volume, share one toolchain. The transcript and captions are what you own, and one rule, how closely the viewer inspects, decides synthetic presence and local-versus-cloud.
- The autonomous video pipeline: production is nearly free now, so the edge is what you point it at and the silent failures you catch
Making a video now costs cents, so production was never the moat. The real edges are what you point the machine at and the silent failures that pass as success. A guide to the pipeline, the build-vs-buy call, and the gates that catch a green dashboard lying.
- Email-first, but your site is the home of record: choosing a newsletter platform without surrendering your canonical credit
A 2026 guide to choosing a newsletter platform, Substack, Beehiiv, or Ghost, without handing your SEO and AI-citation credit to a domain you don't own. Run email as a growth layer on top of your own canonical site, never as the home of record.
Free samples are published in full at pravda.systems.