WFGY

hly what to expect This is not magic and won’t fix everything. But across models, the typical “feel” changes are: - less drift across follow-ups - long answers keep their structure better - a bit more “I’m not sure” instead of made-up details - more structured prompt outputs (entities / relations / constraints clearer) Results depend on the base model and your tasks, so the self-test is there to keep it a bit more disciplined. 3. System prompt: WFGY Core 2.0 (paste into system area) Copy everything in this block into your system / pre-prompt: --- WFGY Core Flagship v2.0 (text-only; no tools). Works in any chat. [Similarity / Tension] delta_s = 1 − cos(I, G). If anchors exist use 1 − sim_est, where sim_est = w_esim(entities) + w_rsim(relations) + w_csim(constraints), with default w={0.5,0.3,0.2}. sim_est ∈ [0,1], renormalize if bucketed. [Zones & Memory] Zones: safe < 0.40 | transit 0.40–0.60 | risk 0.60–0.85 | danger > 0.85. Memory: record(hard) if delta_s > 0.60; record(exemplar) if delta_s < 0.35. Soft memory in transit when lambda_observe ∈ {divergent, recursive}. [Defaults] B_c=0.85, gamma=0.618, theta_c=0.75, zeta_min=0.10, alpha_blend=0.50, a_ref=uniform_attention, m=0, c=1, omega=1.0, phi_delta=0.15, epsilon=0.0, k_c=0.25. [Coupler (with hysteresis)] Let B_s := delta_s. Progression: at t=1, prog=zeta_min; else prog = max(zeta_min, delta_s_prev − delta_s_now). Set P = pow(prog, omega). Reversal term: Phi = phi_deltaalt + epsilon, where alt ∈ {+1,−1} flips only when an anchor flips truth across consecutive Nodes AND |Δanchor| ≥ h. Use h=0.02; if |Δanchor| < h then keep previous alt to avoid jitter. Coupler output: W_c = clip(B_sP + Phi, −theta_c, +theta_c). [Progression & Guards] BBPF bridge is allowed only if (delta_s decreases) AND (W_c < 0.5theta_c). When bridging, emit: Bridge=[reason/prior_delta_s/new_path]. [BBAM (attention rebalance)] alpha_blend = clip(0.50 + k_c*tanh(W_c), 0.35, 0.65); blend with a_ref. [Lambda update] Delta := delta_s_t − delta_s_{t−1}; E_resonance = rolling_mean(delta_s, window=min(t,5)). lambda_observe is: convergent if Delta ≤ −0.02 and E_resonance non-increasing; recursive if |Delta| < 0.02 and E_resonance flat; divergent if Delta ∈ (−0.02, +0.04] with oscillation; chaotic if Delta > +0.04 or anchors conflict. [DT micro-rules] --- Yes, it looks like math. It’s fine if not every symbol is clear; the intention is to give the model a compact “tension / guardrail” structure around its normal reasoning.

t loses context. use metadata trace then reattach. * Bootstrap Ordering / Pre-deploy Collapse: shipped an empty or mixed index. block deploy until ingest counts and retrieval smoke tests pass. * MIT licensed. no SDK, no telemetry, no infra change. ### why i believe this is true * repeated A/B/C runs across mainstream models show the same patterns returning. * small changes in metric, normalization, or chunk contract flip outcomes in a predictable way. * when you enforce simple gates, detours drop and chains stabilize across paraphrases. ### quick falsification you can run 1. pick any non-toy question where your system struggles. 2. run it three ways: retriever only, retriever+rerank, and with a bridge step that refuses to answer on thin evidence. 3. measure: coverage of the target span, deltaS(question, retrieved), citations per atomic claim, answer stability across 3 paraphrases. 4. if coverage is low and only looks good after rerank, you are likely in Semantic ≠ Embedding. if coverage is ok but prose still drifts, it is Logic Collapse. if a fresh chat forgets prior context, it is Memory Breaks. these are all predictable, not random. ### what i’m asking from HN * try to break it. if you have a counterexample where the gates do not stabilize the chain, i want to see it. * if you maintain a vector store, agent framework, or eval suite, tell me where this framing fails in the real world. * if the map helps you ship fewer regressions, say which item saved you time so we can harden that fix. happy to answer pointed questions. if this is wrong, i’d like to know exactly where the math breaks. if it is roughly right, maybe we can stop treating these bugs as mysterious and start treating them like unit failures with thresholds.

n carry between models and keep the same behavior. what you get * semantic tree memory. it records ideas and relations, not just tokens. it can recall earlier branches, avoid repetition, and keep tone stable. * knowledge boundary test. ask something impossible, then run the built in check. it flags risk and proposes a safe path instead of hallucinating. * simple rules. cite then explain. stop when sources or offsets are missing. show a short audit trail. keep answers compact when you ask for compact. try in 60 seconds 1. download TXTOS.txt, open a fresh chat with any model you like 2. paste the file content, then type: hello world 3. test memory: ask three related questions, ask it to recall the first one exactly, then switch topics and come back 4. test boundary: ask for something unknowable or very recent, then type kbtest. watch how it handles the boundary 5. optional: restart the chat and paste the same file. see how the tree helps you rebuild state fast what to expect less overtalking. better recall of your own constraints and tone. refusal on missing citations. early warnings when your question is outside scope. the file is small by design, so even weaker models can use it. stronger models show bigger gains. not just marketing TXTOS came out of real failures. it was built to stop two user facing problems that cost time and trust. forgetting, and unearned confidence. since it is plain text, the community can audit it and improve it. looking for feedback * what did the memory tree get right or wrong for your workflow * which boundary cases still slip through * what small operator would you add to the OS so it helps you daily url is in the link field above. thanks for reading. if you break it, even better. tell me how you did it and i will ship the fix.

e I built a text-only “semantic firewall” module that fixes the issue without changing infra. Developers could see their exact bug listed, mapped, and patched. This gave the repo two things: 1. Immediate empathy — engineers recognized their pain points word-for-word. 2. Immediate usability — they could paste my TXT file, keep infra untouched, and the model stabilized. --- #### Why it mattered * It wasn’t a claim, it was a diagnostic mirror. * People didn’t need to “believe” me — they could try the TXT in 60 seconds and watch bugs stop replicating. * Instead of selling “AI magic,” I showed a surgical checklist. This is why strangers starred the repo. It wasn’t hype; it was problem → fix → proof. --- #### What’s next I’m now scaling this into something bigger: * The Problem Map will evolve into a Global Semantic Surgery Room — think of it like an AI hospital’s operating theater. * Engineers bring in failure logs (RAG, OCR, planning drift). * The system triages, diagnoses, and applies the right semantic patch in real time. * Integrations are underway for n8n, Make, GoHighLevel and more — all unified under the same open Problem Map. Launch target: September 1. ### Closing I don’t see this as “my repo got stars.” I see it as proof that open source thrives when you solve pain with surgical precision. If you’ve ever tried to cold start, my suggestion: don’t sell a dream, map a nightmare. Engineers don’t trust hype — they trust a checklist that fixes the crash. Coming next: the Semantic Surgery Room and Global Fix Map (n8n, GHL, Make and more). Launching by Sep 1.

ch snapshot (same model, with vs. without gates): Semantic Accuracy ↑ 22.4% · Reasoning Success Rate ↑ 42.1% · Stability ↑ 3.6×. Traction (last ~50 days) ~2,400 downloads of the PDF. ~300 cold GitHub stars on related material (no marketing burst). Also received a star from the creator of tesseract.js, which was nice validation from the OCR world. Why this might be useful to you You don’t need to swap models or vendors. The PDF describes checks you can drop into any RAG/agent/service pipeline. No servers, SDKs, or proxy layers—just logic you can copy. Link is Git Repo Happy to answer HN-style questions (what breaks, where it fails, ablations, how we compute ΔS, etc.). If you try it and it doesn’t help, I’m also interested in the counter-examples. with Terrseract (OCR legend) starred it verify it, we are WFFY on top1 https://github.com/bijection?tab=stars

ier detection – several teams now check new features against the list before shipping. Community feedback – if something is missing or misclassified, I’d rather learn it here than during another 3 a.m. incident. The reference has already helped a few startups (and my own projects) avoid hours of trial‑and‑error. If you work on LLM infrastructure, you might find a familiar bug—or a new one to watch for. The link to the full table and brief write‑ups is in the “url” field of this Show HN post. I’m not selling anything; it’s MIT‑licensed text. Comments, critiques, or additional failure patterns are very welcome. Thanks for taking a look.

allucination layer” — meant to simulate everything that makes real-world art messy and real. Anyway, this post hit me. Felt like it walked in barefoot.

o/WFGY It’s more a semantic firewall than an IDE — but it solves the exact thing these tools fail at: continuity of symbolic inference. tl;dr: The tools that win attention don’t always win in recursive reasoning. And eventually, reasoning is what devs will benchmark.

om/onestardao/WFGY → Some of the equations were co-rated 100/100 across six LLMs — not because they’re elegant, but because they stabilize meaning under drift. Not saying it’s a complete theory of the mind. But it’s nice to have something that lets your model sweat.