swyx @swyx 11h one thing i think people dont appreciate enough about @poolsideai is their unusual degree of openness ...
AI ‘model factories’ and coding-model arms race keep the infra upcycle intact (compute + networking + data center buildout).
Linked assets
These are the assets attached to this thesis, along with direction, confidence, and outcome so far.
NVIDIA Corporation operates as a data center scale AI infrastructure company.
Best direct proxy to training/inference scaling from faster iteration cycles.
ANET is Arista Networks, Inc., a Technology-sector equity in the Computer Hardware industry, focused on networking solutions for data centers and enterprises.
AI clusters require higher-bandwidth networking as experiment throughput rises.
Broadcom Inc.
Exposure to AI data-center components/interconnect; benefits from sustained scaling.
Super Micro Computer, Inc., together with its subsidiaries, develops and sells server and storage solutions based on modular and open-standard architecture in the United States, A…
Server buildout levered to AI capex cycle, though more volatile/competitive.
Source proof
Source proof: Strong source proof | 4 extracted claims | 4 directional assets | 1 supporting author | headline-like title review
A developer reports dogfooding an “agentic GitHub clone” with built-in CI/CD enabled by “Workers for Platforms” (likely Cloudflare). This is anecdotal but directionally supports a thesis that edge/serverless platforms are becoming credible devtool primitives and could expand into adjacent workflows (code hosting, CI/CD, agentic automation).
Social post highlights PoolsideAI’s rapid iteration (“10k–20k experiments/month”), focus on coding models as a path to AGI, and unusual openness/open-model posture. No public-company financials disclosed, but it reinforces the ongoing buildout/competition in code-gen LLMs and the need for large-scale AI training/inference infrastructure.
The source is a brief social comment about LLM “adherence” (instruction-following) being primarily determined in post-training, not architecture changes like “just appending it.” It contains no market, company, product, or financial claims tied to public tickers, and provides no concrete catalyst, timing, or trade setup.
Brief comment referencing "oai" (likely OpenAI) and that something is "all about posttraining"; no concrete market, product, or company details provided.
The post contains no market, company, macro, or product information beyond “talk to shands” and a link. It does not provide tradable insights or identifiable catalysts.
The provided content is a short social-media mention with a link but contains no market, company, macro, or financial information that can be evaluated for investment implications.
The provided text contains no market, macro, sector, or company-specific information beyond a generic agreement. There are no actionable claims, catalysts, or identifiable tradable implications.
The source is a developer-to-developer question about offering external-facing APIs for agents/CLIs to operate an app. It contains no market, company, product, regulatory, financial, or macro information and provides no ticker-relevant catalyst.
Supporting authors
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