Writing
Essays and open-source releases from the team. No hot takes, no vendor pitch — just data and patterns from systems we run ourselves.
Some stages of a goal cannot be skipped, whoever does the work. How BPOS writes them down as a program, how BPOS Lite was built through one, what a necessity is, and where we are behind the research.
Read →How a system learns from its own work without anyone typing the answer, in reading order. Ten pieces; the Knowledge Layer is what they add up to.
How the system around the model is built: gates, scaffolds, context delivery, and why structure beats instruction.
Some stages of a goal cannot be skipped, whoever does the work. How BPOS writes them down as a program, how BPOS Lite was built through one, what a necessity is, and where we are behind the research.
For a year we wrote about what we learned, not about the machine that taught us. This is the machine: how a goal becomes shipped code, what decides what ships, and what it does not do.
Google's SDLC paper says the work is moving to specification and verification. A second Google paper says a hand-written specification can make an agent worse. Both are right, and the gap between them is where we work.
A wiki is knowledge you go to. Working knowledge is knowledge you look through. The difference decides whether an AI agent uses what your company knows, or ignores it.
Three kinds of claim live in every company. A knowledge base that stores them as one kind will learn nonsense, and so will the AI that reads it.
A soap film, a slime mould and a pigeon each add one thing to a rule that learns. The law BPOS uses, why it resists superstition, what our own graph has actually earned, and the evidence against us.
We decide how BPOS learns. The work decides what it learns. The three ways a learned weight can move, why one is locked, and the eight days that taught us.
Given no guidance, an AI agent scored 87.50 on a benchmark task. Given an expert’s map of the task, it scored 58.93. What repaired it, and where our own system still falls short.
A passing pipeline proves the code compiled and the tests you wrote passed. It does not prove the work does what was asked, or say why it worked. Across 12,103 recorded gate decisions and 331 sourced rejection notes, that difference is where AI-written software quietly fails.
Reasoning traces from a third-party API are a safety surface you rent, not own. Building enforcement on them is faith, not verification. Three beats from our own stack: a trace reader, dependency ordering, and a portable test that survives a provider change.
We put a mechanical grounding gate in front of our plan generator and watched violations fall from nine to zero across seven generations — without loosening the gate once. The last two defects lived in the generator's own scaffold and in the judge itself.
We bet the system around the model matters more than the model itself. Over the last few months, five independent practitioners and a frontier lab arrived at the same conclusion — none of them ours.
Lazy skill loading for agent systems. Claude Skills are useful when loaded on demand, expensive when loaded up front. context-steward is the npm package that solves the second problem without breaking the first.
Multi-agent delivery doesn't break on the model. It breaks on file conflicts, bad sequencing, and per-job metrics that miss the deployment. The unglamorous machinery that decides whether a sprint ships.
Measured findings, including the ones that said no. Nulls published next to wins — the method is the product.
An AI assistant told us our system could not do eight things. Seven of those limits were never there. How to tell a perceived limitation from a real one before you build a workaround.
LLM-as-a-judge has a failure mode nobody warns you about: saturation. Our conformance judge returned exactly 1.00 on roughly 92% of non-failed runs, looked statistically precise, and scored a fabricated output and a faithful one identically. The measurement, and the vector that replaced the single score.
One incident, fully instrumented: a reviewer wrote a plausible causal theory into rejection feedback, the model obeyed the theory, and the retry regressed. Why the feedback channel carries sourced facts only. An n of one, stated as one.
We built skill checklists for our agents, believed in them, and measured them anyway. Across 88 arm-randomised scored executions, quality moved +0.002 — inside the noise. The null, the method that caught it, and the boundary on what it does not say.
A running record of independent practitioners, an academic study, and frontier labs arriving at the conclusions we built on — in their own words, with dates, sources and caveats. Updated as the record grows.
We audited 36 mechanical quality checks and found about 20 had caught nothing in a month of production. The scaffolding you build for a weak model becomes a cage for a strong one.
A GDPR-certified consent tool got a client fined anyway — because blocking a tracker doesn't make you invisible, it makes you a specific, reconstructable kind of missing data. On MNAR statistics, Bayesian imputation, and what enforcement actually catches.
Per-job quality scores in multi-agent systems almost always land around 85%. That number is telling you something different than you think it is. 4,600+ scored executions from our own production, and the pattern we found.
We ran a controlled A/B across 88 scored agent executions, with and without skill checklists in the prompt. Quality moved +0.002 — inside the noise. Why front-loaded instructions don't land, and what does.
Listening to production: telemetry, failure surfacing, and the feedback loops that turn incidents into structure.
Our supervised agent platform ran a night of maintenance on itself. Five defects came out, zero of them model failures. The strongest warning in the system was written by a worker, for free, into a table nothing reads.
Server-side click attribution: record the click at the link, before the browser can drop it. How HikrLink does it, the one thing it refuses to do, and the three wiring bugs we found joining it to our own tracker — none on the link side.
SEO as an instrumentation problem: four sensors, deconvolution that checks its own arithmetic, a hysteresis machine cleared through a four-run backtest, and a publisher that mechanically says no. Every decision leaves a receipt.
The same failure surfaced twice in one day: 47 minutes in the morning, 6 seconds in the evening — a ~470× difference. Five changes, one principle: listen to the system's own signals.