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compounding math problem on local models. 90% per-step accuracy sounds great, but with a 5-step workflow that's a 40% failure rate. No existing framework seemed to address this mechanical reliability issue - they all seemed tailor-made for cloud frontier.<p>Demo video: <a href=\"https://youtu.be/MzRgJoJAXGc\" rel=\"nofollow\">https://youtu.be/MzRgJoJAXGc</a> (side-by-side: same model, same task, with and without <em>Forge</em> guardrails)<p>The paper (accepted to ACM CAIS '26, presenting May 26-29 in San Jose) covers the peer-reviewed findings across 97 model/backend configurations, 18 scenarios, 50 runs each. Key numbers:<p>- Ministral 8B with <em>Forge</em>: 99.3%. Claude Sonnet with <em>Forge</em>: 100%. The gap between a free local 8B model on a $600 GPU and a frontier API is less than 1 point.<p>- The same 8B local model with <em>Forge</em> (99.3%) outperforms Claude Sonnet without guardrails (87.2%) - an 8B model with framework support beats the best result you can get through frontier API alone.<p>- Error recovery scores 0% for every model tested - local and frontier - without the retry mechanism. Not a capability gap, an architectural absence.<p>I'm currently using this for my home assistant running on Ministral 14B-Reasoning, and for my locally hosted agentic coding harness (8B managed to contribute to the codebase!).<p>The guardrail stack has five layers, each independently toggleable. The two that carry the most weight (per ablation study with McNemar's test): retry nudges (24-49 point drops when disabled) and error recovery (~10 point drops, significant for every model tested). Step enforcement is situational - only fires for models with weaker sequencing discipline. Rescue parsing and context compaction showed no significance in the eval but are retained for production workloads where they activate once in a while.<p>One thing I really didn't expect: the serving backend matters. Same Mistral-Nemo 12B weights produce 7% accuracy on llama-server with native function calling and 83% on Llamafile in prompt mode. A 75-point swing from infrastructure alone. I don't think anyone's published this because standard benchmarks don't control for serving backend.<p>Another surprise: there's no distinction in current LLM tool-calling between &quot;the tool ran successfully and returned data&quot; and &quot;the tool ran successfully but found nothing.&quot; Both return a value, the orchestrator marks the step complete, and bad data cascades downstream. It's the equivalent of HTTP having 200 but no 404. <em>Forge</em> adds this as a new exception class (ToolResolutionError) - the model sees the error and can retry instead of silently passing garbage forward.<p>Biggest technical challenge was context compaction for memory-constrained hardware. Both Ollama and Llamafile silently fall back to CPU when the model exceeds VRAM - no warning, no error, just 10-100x slower inference. <em>Forge</em> queries nvidia-smi at startup and derives a token budget to prevent this.<p>How to try it:<p>- Clone the repo, run the eval harness on a model I haven't tested. If you get interesting results I'll add them to the dashboard.<p>- Try the proxy server mode - point any OpenAI-compatible client at <em>Forge</em> and it handles guardrails transparently. It's the newest model and I'd love more eyes on it.<p>- Dogfooding led me to optimize model parameters in v0.6.0. The harder eval suite (26 scenarios) is designed to raise the ceiling so no one sits at 100%. Several that did on the original suite can't sweep it - including Opus 4.6. Curious if anyone finds scenarios that expose gaps I haven't thought of. Paper numbers based on pre v0.6.0 code.<p>Background: prior ML publication in unsupervised learning (83 citations). This paper accepted to ACM CAIS '26 - presenting May 26-29.<p>Repo: <a href=\"https://github.com/antoinezambelli/forge\" rel=\"nofollow\">https://github.com/antoinezambelli/<em>forge</em></a><p>Paper: <a href=\"https://www.caisconf.org/program/2026/demos/forge-agentic-reliability/\" rel=\"nofollow\">https://www.caisconf.org/program/2026/demos/<em>forge</em>-agentic-re...</a> <a href=\"https://github.com/antoinezambelli/forge/blob/main/docs/forge_ieee_preprint.pdf\" rel=\"nofollow\">https://github.com/antoinezambelli/<em>forge</em>/blob/main/docs/forg...</a><p>Dashboard: <a href=\"https://github.com/antoinezambelli/forge/docs/results/dashboard.html\" rel=\"nofollow\">https://github.com/antoinezambelli/<em>forge</em>/docs/results/dashbo...</a>"},"title":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["forge"],"value":"Show HN: <em>Forge</em> \u2013 Guardrails take an 8B model from 53% to 99% on agentic tasks"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["forge"],"value":"https://github.com/antoinezambelli/<em>forge</em>"}},"_tags":["story","author_zambelli","story_48192383","show_hn"],"author":"zambelli","children":[48197802,48198479,48198514,48198634,48198706,48198875,48199115,48199142,48199417,48199806,48199948,48200002,48200090,48200124,48200234,48200286,48200304,48200330,48200359,48200762,48200832,48200953,48201036,48201398,48201421,48201422,48201622,48201768,48201888,48202111,48202167,48202227,48202361,48202380,48202590,48203121,48203145,48203298,48203387,48203439,48203590,48203596,48203742,48203774,48203782,48203854,48203968,48203974,48204143,48204259,48204391,48204464,48204487,48204493,48204515,48204556,48205229,48205302,48205733,48205781,48206044,48206436,48206492,48206574,48208442,48208854,48208866,48209241,48209380,48209894,48209974,48210301,48211054,48211145,48213619,48217110,48217211,48221239,48222249,48222279,48223509,48235392,48238617,48238932,48249395,48250938,48264115,48277770,48279760,48304747],"created_at":"2026-05-19T12:23:07Z","created_at_i":1779193387,"num_comments":252,"objectID":"48192383","points":687,"story_id":48192383,"story_text":"Hi HN, I&#x27;m Antoine Zambelli, AI Director at Texas Instruments.<p>I built Forge, an open-source reliability layer for self-hosted LLM tool-calling.<p>What it does:<p>- Adds domain-and-tool-agnostic guardrails (retry nudges, step enforcement, error recovery, VRAM-aware context management) to local models running on consumer hardware<p>- Takes an 8B model from ~53% to ~99% on multi-step agentic workflows without changing the model - just the system around it<p>- Ships with an eval harness and interactive dashboard so you can reproduce every number<p>I wanted to run a handful of always-on agentic systems for my portfolio, didn&#x27;t want to pay cloud frontier costs, and immediately hit the compounding math problem on local models. 90% per-step accuracy sounds great, but with a 5-step workflow that&#x27;s a 40% failure rate. No existing framework seemed to address this mechanical reliability issue - they all seemed tailor-made for cloud frontier.<p>Demo video: <a href=\"https:&#x2F;&#x2F;youtu.be&#x2F;MzRgJoJAXGc\" rel=\"nofollow\">https:&#x2F;&#x2F;youtu.be&#x2F;MzRgJoJAXGc</a> (side-by-side: same model, same task, with and without Forge guardrails)<p>The paper (accepted to ACM CAIS &#x27;26, presenting May 26-29 in San Jose) covers the peer-reviewed findings across 97 model&#x2F;backend configurations, 18 scenarios, 50 runs each. Key numbers:<p>- Ministral 8B with Forge: 99.3%. Claude Sonnet with Forge: 100%. The gap between a free local 8B model on a $600 GPU and a frontier API is less than 1 point.<p>- The same 8B local model with Forge (99.3%) outperforms Claude Sonnet without guardrails (87.2%) - an 8B model with framework support beats the best result you can get through frontier API alone.<p>- Error recovery scores 0% for every model tested - local and frontier - without the retry mechanism. Not a capability gap, an architectural absence.<p>I&#x27;m currently using this for my home assistant running on Ministral 14B-Reasoning, and for my locally hosted agentic coding harness (8B managed to contribute to the codebase!).<p>The guardrail stack has five layers, each independently toggleable. The two that carry the most weight (per ablation study with McNemar&#x27;s test): retry nudges (24-49 point drops when disabled) and error recovery (~10 point drops, significant for every model tested). Step enforcement is situational - only fires for models with weaker sequencing discipline. Rescue parsing and context compaction showed no significance in the eval but are retained for production workloads where they activate once in a while.<p>One thing I really didn&#x27;t expect: the serving backend matters. Same Mistral-Nemo 12B weights produce 7% accuracy on llama-server with native function calling and 83% on Llamafile in prompt mode. A 75-point swing from infrastructure alone. I don&#x27;t think anyone&#x27;s published this because standard benchmarks don&#x27;t control for serving backend.<p>Another surprise: there&#x27;s no distinction in current LLM tool-calling between &quot;the tool ran successfully and returned data&quot; and &quot;the tool ran successfully but found nothing.&quot; Both return a value, the orchestrator marks the step complete, and bad data cascades downstream. It&#x27;s the equivalent of HTTP having 200 but no 404. Forge adds this as a new exception class (ToolResolutionError) - the model sees the error and can retry instead of silently passing garbage forward.<p>Biggest technical challenge was context compaction for memory-constrained hardware. Both Ollama and Llamafile silently fall back to CPU when the model exceeds VRAM - no warning, no error, just 10-100x slower inference. Forge queries nvidia-smi at startup and derives a token budget to prevent this.<p>How to try it:<p>- Clone the repo, run the eval harness on a model I haven&#x27;t tested. If you get interesting results I&#x27;ll add them to the dashboard.<p>- Try the proxy server mode - point any OpenAI-compatible client at Forge and it handles guardrails transparently. It&#x27;s the newest model and I&#x27;d love more eyes on it.<p>- Dogfooding led me to optimize model parameters in v0.6.0. The harder eval suite (26 scenarios) is designed to raise the ceiling so no one sits at 100%. Several that did on the original suite can&#x27;t sweep it - including Opus 4.6. Curious if anyone finds scenarios that expose gaps I haven&#x27;t thought of. Paper numbers based on pre v0.6.0 code.<p>Background: prior ML publication in unsupervised learning (83 citations). This paper accepted to ACM CAIS &#x27;26 - presenting May 26-29.<p>Repo: <a href=\"https:&#x2F;&#x2F;github.com&#x2F;antoinezambelli&#x2F;forge\" rel=\"nofollow\">https:&#x2F;&#x2F;github.com&#x2F;antoinezambelli&#x2F;forge</a><p>Paper: <a href=\"https:&#x2F;&#x2F;www.caisconf.org&#x2F;program&#x2F;2026&#x2F;demos&#x2F;forge-agentic-reliability&#x2F;\" rel=\"nofollow\">https:&#x2F;&#x2F;www.caisconf.org&#x2F;program&#x2F;2026&#x2F;demos&#x2F;forge-agentic-re...</a> <a href=\"https:&#x2F;&#x2F;github.com&#x2F;antoinezambelli&#x2F;forge&#x2F;blob&#x2F;main&#x2F;docs&#x2F;forge_ieee_preprint.pdf\" rel=\"nofollow\">https:&#x2F;&#x2F;github.com&#x2F;antoinezambelli&#x2F;forge&#x2F;blob&#x2F;main&#x2F;docs&#x2F;forg...</a><p>Dashboard: <a href=\"https:&#x2F;&#x2F;github.com&#x2F;antoinezambelli&#x2F;forge&#x2F;docs&#x2F;results&#x2F;dashboard.html\" rel=\"nofollow\">https:&#x2F;&#x2F;github.com&#x2F;antoinezambelli&#x2F;forge&#x2F;docs&#x2F;results&#x2F;dashbo...</a>","title":"Show HN: Forge 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