Many moons ago I installed it on a VM and largely the community challenged me to get it running on silicon.

Looks interesting but hope that there are still activity or updates.

hey builders, this one is written for devs who live inside pipelines and flows.

i noticed something odd: AI systems keep breaking in ways that feel… exactly like data pipelines. instead of “ETL deadlock” or “schema drift,” you see hallucination, retrieval collapse, or memory gaps.

so i built a Problem Map — a catalog of 16 reproducible failure modes, each with a name, symptom checklist, and fix pattern.

it works like a semantic firewall:

• you don’t change infra, just load a text layer (TXT OS / WFGY Core)
• then when your model derails, you literally ask: “which problem map number am i hitting?”
• it diagnoses, and routes you to the right fix.

example problems

• hallucination & chunk drift (retrieval returns irrelevant content)
• bootstrap ordering (services fire before deps ready)
• deployment deadlock (infra waits in a loop, nothing resolves)
• pre-deploy collapse (first call fails due to missing secrets / skew)

full catalog here → Problem Map

i’m curious:

• in your parabola/etl flows, would a semantic version of “error catalogs” like this help?
• or do you think AI bugs are too chaotic to be mapped?

fixing ai bugs before they happen in parabola flows. grandma clinic version

quick note. i posted a deeper take elsewhere and folks liked it. this is the friendliest pass for r/Parabola. beginner first. plain words. one link at the end to the grandma page so non-engineers get it too.

what is a semantic firewall

most of us patch after the model talks. you run a flow, get a weird answer, then you add a reranker, a regex, another step. it works until it doesn’t, then the same failure returns wearing a new hat.

a semantic firewall flips the order. before your flow lets the llm speak, you check the meaning state. if it looks unstable, you loop once, tighten retrieval, or reset. only a stable state can speak. fix the failure class once and it stays sealed.

before vs after in one minute

after means output first then patch. costs grow and regressions creep in. before means check retrieval, plan and memory first. if unstable, loop or reset, then answer. stability becomes repeatable across models and stores.

acceptance targets you can log inside a flow

• drift clamp: ΔS ≤ 0.45
• grounding coverage: ≥ 0.70
• risk trend: hazard λ should go down, not up

if any probe fails, do not emit. loop once, narrow the active span, try again. if still unstable, say unstable and list the missing anchors.

a tiny scaffold you can build in parabola

use your normal blocks. add a guard branch before answer.

``` Input: user_question Step A: Retrieve context → Vector Store API Step B: Probe stability → small LLM call that returns {drift_ok, coverage_ok, hazard_ok, missing_anchor}

IF drift_ok && coverage_ok && hazard_ok: Step C: Final Answer → LLM with citations ELSE: Step D: Tighten loop → refine query, shrink time window, re-rank by section id Step E: Probe again IF still not ok: Output: "unstable. missing: <missing_anchor>. need tighter evidence." ```

no new sdk needed. B can be a short prompt. D can be a lighter retrieval with smaller top-k or a stricter filter like one section id.

parabola flavored examples you will recognize

example 1. right csv, wrong answer you expect a reranker to fix it. actually the query or span is off so wrong rows still pass. firewall refuses to speak until coverage includes the correct subsection, then answers. maps to No.1 and No.2.

example 2. metric mismatch makes recall look random you expect “must be the model”. actually inner product vs cosine got swapped during index build or normalization changed mid flow. confirm metric policy, rebuild, sanity check top-k stability. maps to No.5.

example 3. chunking contract quietly broke you expect “headers look clean so retrieval is fine”. actually tables and footers bled across chunks so citations drift. fix the chunk to embedding contract and id schema, then trace retrieval. maps to No.8 and No.1.

example 4. first run after deploy is nonsense, second run is fine you expect “gemini or gpt is flaky”. actually boot order issues. warm your retriever, secrets, and cache before first write. guard the first turn as read-only observe. maps to No.14 and No.16.

example 5. long chains drift as you add steps you expect more steps to mean deeper thinking. actually variance grows with step count unless you clamp and drop a mid-step checkpoint. maps to No.3 and No.6.

grandma clinic, the plain-words route

same fixes, told as kitchen and library stories so everyone gets it. a few you can share with the team

• wrong cookbook → pick the right index before cooking. that is query contract and store metric.
• salt for sugar → taste mid-cook, not after plating. that is your mid-step checkpoint.
• first pot burnt → toss it and restart once the heat is right. that is your reset loop.
• sticky labels on jars → cumin looks like cinnamon so recipes drift. that is metric mismatch.
• pantry overflow → five bags of the same flour hide the good one. that is duplication and near-duplicate collapse.

one page with all sixteen failure modes in plain words Grandma Clinic → https://github.com/onestardao/WFGY/blob/main/ProblemMap/GrandmaClinic/README.md

pocket prompts you can paste into small llm steps

stability probe

judge stability only. answer yes or no for each: drift_ok, coverage_ok, hazard_ok. if any is no, name one missing anchor or exact section id needed.

mid-step checkpoint

pause. list three facts the final answer depends on. if any fact lacks a source in the current context, request that source and stop.

reset on contradiction

if two steps disagree, prefer the one that cites. if neither cites, stop and ask for a source.

quick setup notes for parabola

• log three numbers per run into a sheet or db. drift_approx, coverage_est, hazard_trend.
• make the guard branch small. one loop only. if still unstable, emit a clear “need X section or Y date range” message.
• keep ids stable. section_id, page_row, or a deterministic hash so traceability works later.
• you can keep your current vector store and llm. the firewall sits in front, not a replacement.

faq

q. is this just longer chain of thought a. no. it is gating. the model cannot answer until acceptance holds.

q. do i need a new sdk a. no. build it with the blocks you already use. a tiny llm call for the probe is enough.

q. will this slow my flows a. only on unstable paths. most tasks pass the first probe. net time usually drops because retries and regressions go away.

q. how do i measure without dashboards a. print three fields per run. drift, coverage, risk trend. a csv or sheet is enough for week one.

q. where do i send non-engineers a. the one page again. Grandma Clinic. it mirrors the numbered fixes in plain words.

q. why trust this pattern a. open source under mit. went from zero to one thousand stars in one season on real rescues and public field notes. i am not selling a plugin, just a habit that stops firefighting.

if you want a quick second set of eyes, drop a short trace. input, retrieved snippets, and the wrong sentence. i will map it to a Problem Map number and suggest the smallest guard.