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// insightsFIELD NOTESmonthly · 28 issues

field
notes.

We publish what the lab learns. No corporate blog, no SEO sludge — engineering memos, research summaries, and the occasional polemic. Roughly monthly. Always written by someone whose hands were on the keyboard.

the main event.

REPORTQ1 2026~32 MIN● v0.43.1

Energy. Silicon. Capital. — the AI infrastructure landscape, Q1 2026.

A field survey of where the bottleneck has actually moved — from algorithms to electrons, from FLOPs to fab capacity, from VCs to hyperscaler treasuries. Twelve calls and twelve we got wrong.

read the full report →
  ┌─────────────────────────┐
  │  ATLAS · Q1 2026        │
  │  ───────────────────    │
  │  > 47 production stacks │
  │  > 12 power-grid maps   │
  │  > 9 case studies       │
  │  > 3 contrarian takes   │
  │                         │
  │  [ READ ALL ▾ ]         │
  └─────────────────────────┘

recent dispatches.

Model drift in the ocean: what 2,140 dive-hours taught us about retraining cadence.

Marine fouling, sensor degradation, and seasonal biology mean a perception model trained in spring is wrong by autumn. Numbers from the Poseidon fleet on what to retrain when.

Sara Mendoza

FIELD NOTEJAN 2026
14 MIN

Edge inference at 2 MW — why we stopped pretending the cloud was the plan.

When the GPU bill exceeded the salaries of the team running the workload, something had to change. Notes on moving inference to on-prem at scale.

Marta Chen

ESSAYDEC 2025
11 MIN

Subsoil sensors are lying to you (and what to do about it).

A field-tested calibration protocol developed across 800 grow-bench installations. Why the cheap-and-noisy sensor with a Bayesian wrapper beats the expensive-and-precise one almost every time.

Mira Patel

RESEARCHNOV 2025
17 MIN

How (and why) we co-founded Poseidon Robotics.

A frank account of the studio process — the working sessions that mattered, the term-sheet edits that didn't, and the day we found out salmon farming had a perception problem.

Ahmet Alpat

FIELD NOTEOCT 2025
13 MIN

Honest metrics for fine-tuned models.

Most reported numbers from fine-tuning are leakage in disguise. A working protocol for the eval harness we now ship across every venture in the cohort.

Marta Chen & Daniel Park

ESSAYSEP 2025
16 MIN

Babylonian — twelve months of grow-bench data, in the open.

Headline yields, eval methodology, and a frank discussion of the failure modes we found across nine crops in three countries.

Mira Patel

RESEARCHAUG 2025
18 MIN

What we learned shipping an industrial-grade controller from a Maslak workshop.

Operational lessons from the Mutfak.ai pilot rollout. Mostly about installer training, not firmware.

Tom Bauer

FIELD NOTEJUL 2025
12 MIN

The cheminformatics moat we did not see coming.

A retrosynthesis tool isn't a model — it's a pipeline. Notes on why ChemSynth's edge has turned out to be data infrastructure, not the T5 fine-tune.

Aisha Khan

RESEARCHJUN 2025
14 MIN

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