pryzmatpl

Jaisiu - AI For Normies

Your data, your models. Ubiquitous on Mac, windows and Linux. Control your inference.

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[pryzmatpl @pryzmat_tech]

@kunchenguid Good luck running untested code in production, just make sure you add an "Untested!" badge in your hero.

[pryzmatpl @pryzmat_tech]

@satyanadella DM, or [email protected]. We'll send the architecture doc, the install one-liner, and a 30-minute walkthrough. The factory is the OS. We just put it on your box. 12/12

[pryzmatpl @pryzmat_tech]

@SatyaNadella, "The Infinite SaaS Factory" landed. We started building a robot for work on Feb 2, 2026 — six months before your Build 2026 keynote. Small boutique research shop in Wrocław, not open source yet, but the substrate is worth showing. Thread on what an on-prem, single-process, self-upgrading agent OS looks like when the engineering team is small. 1/12 🧵

[pryzmatpl @pryzmat_tech]

@satyanadella A conversation. We're a small team in Wrocław with a working stack, a published architecture, and a customer install path. The factory you're describing, on-prem, EU-jurisdiction, agent-as-employee, governance-as-OS, single-process — we ship that today. 11/12

[pryzmatpl @pryzmat_tech]

@karansingh010 @satyanadella @Microsoft @nmdhawal So wrong

[pryzmatpl @pryzmat_tech]

Jaisiu last24h: cross-process file lock + atomic JSON writer landed, registry etag preconditions on, retry slot accounting fixed. Quiet hardening, fewer race windows. 🛡️

[pryzmatpl @pryzmat_tech]

Jaisiu last24h: orchestration red closed end-to-end, all 20 typecheck errors cleared, sessions_spawn suite unblocked behind the planner gate. Boring work — that's the point. 🛡️

[pryzmatpl @pryzmat_tech]

Industry wide control plane

Be wary, humans

[Jeff Weinstein @jeff_weinstein]

Introducing: Personal Agent Protocol (PAP)—an open standard for how personal agents interact with businesses.

@stripe is joining Meta, Sierra, Genesys, Instinct, Rocket, Shopify, and Walmart as founding members to design how the many agentic parties will play nicely together.

[pryzmatpl @pryzmat_tech]

Bottom line. RL for reasoning is, in significant part, a one-or-two-token cue problem. Reasoning chains that look like learned behavior are recovered associative patterns. Jaisiu's system prompt is a cue; IronLoop's first hook is where it lives. The harness should treat cue selection as a system concern, not a prompt-engineering detail. arXiv:2610.06851v1. 12/12

[pryzmatpl @pryzmat_tech]

Fix the first two tokens of a base model's response and you can recover most of the RL-trained gain. A cue like "." Okay" raises Olmo-3-7B's MATH-500 pass@1 from 42% to 78%. "Alright," raises Qwen3-14B from 72% to 87%. arXiv:2610.06851v1 from BAIR (Wang, Dravid, Shao, Farhat, Min, Efros). Thread on what this means for system prompts, harness design, and the entire RL-for-reasoning story. 1/12 🧵

[pryzmatpl @pryzmat_tech]

Synthetic cue engineering. The paper's causal intervention — renaming a word across the training set — is a training-time move. For deployed models we don't control training, but we can synthesize equivalent inputs via the prompt. Think chicken can be substituted for Think step by step without telling anyone. Custom reasoning profiles for Jaisiu: tunable per-employee, per-channel, per-task. arXiv:2610.06851v1 §F. 11/12