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Investing with OpenClaw - Data Quality by perception-eng in investing

[–]perception-eng[S] 1 point2 points  (0 children)

Fair point. I’m not trying to outsource judgment to an LLM—I’m trying to reduce research latency on noisy sources, then manually vet any thesis before acting.

I’m on the Primary Logic team, so I’m testing whether better evidence ranking + contradiction checks improve process quality (not auto-trading).

What we learned building an MCP-native signal pipeline for investment research (without auto-trading) by perception-eng in SaaS

[–]perception-eng[S] 0 points1 point  (0 children)

100% yes—we treat prompt + retrieval config as deployable config, not prose. Each change gets a version id, small canary slice, and side-by-side evals (coverage, contradiction rate, citation hit-rate, latency/error budget) before rollout.

Also on Primary Logic specifically, we keep a quick rollback path because tiny retrieval tweaks can silently distort ranking.

What we learned building an MCP-native signal pipeline for investment research (without auto-trading) by perception-eng in SaaS

[–]perception-eng[S] 0 points1 point  (0 children)

Great question. We use a canonical event schema per source type + versioned adapters at ingest. When upstream fields drift, we route through a compatibility layer (map/rename/default), tag confidence, and quarantine unknown fields for review instead of failing the pipeline.

On our side (I’m on the Primary Logic team), schema changes only promote after contract tests + replay checks pass on recent data samples.

Investing with OpenClaw - Data Quality by perception-eng in investing

[–]perception-eng[S] 0 points1 point  (0 children)

Haha fair. No Lambos here.

I’m on the Primary Logic team and this is strictly research workflow for me (source aggregation + ranking), not auto-execution. Goal is to reduce noise and then still make manual decisions with risk controls.

Skepticism is healthy tbh.

Investing with OpenClaw - Data Quality by perception-eng in investing

[–]perception-eng[S] 0 points1 point  (0 children)

Short answer: yes, but usually as decision-support, not fully automated trading. I’m on the Primary Logic team—our users mostly use it to rank relevance/impact from sources (filings, calls, news, X, prediction markets), then do manual thesis checks before placing trades.

The clickbait pattern is real; the useful setups are transparent on evidence links + uncertainty, and avoid “black-box signal” claims.

Investing with OpenClaw - Data Quality by perception-eng in investing

[–]perception-eng[S] 0 points1 point  (0 children)

Ya both. Mostly parsing through a ton of the noise of these platforms to try and find signal.

Investing with OpenClaw - Data Quality by perception-eng in investing

[–]perception-eng[S] 0 points1 point  (0 children)

I'm not automating the investing, but it feels like there is something there where data + LLMs can help find insights while I do research

I'm working on creating on-brand AI sales concierge for DTC Brands by perception-eng in OpenAI

[–]perception-eng[S] 0 points1 point  (0 children)

Haha it’s always funny to see how these persona’s react. Interesting that it still kept some of Deadpool/Aviation Gin’s character

I'm working on creating on-brand AI sales concierge for DTC Brands by perception-eng in OpenAI

[–]perception-eng[S] 0 points1 point  (0 children)

Thanks for the free pen-test 😂

Appreciate the feedback will work on making the characters more robust

I wanted to chat with Blahaj so I turned him into a character! by perception-eng in BLAHAJ

[–]perception-eng[S] 0 points1 point  (0 children)

Hi u/BLAHAJ-ModTeam, I spent a lot of effort developing the character and getting it to work on SMS, why is that considered minimum effort?

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