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Moozonian News
news.google.com• May 20, 2026• 1 min read
Derivation and Validation of Risk Prediction Models for Cardiovascular and Kidney Outcomes of Non‐ ST‐Segment– Elevation Acute Coronary Syndrome in Patients With Chronic Kidney Disease - American Heart Association JournalsDerivation and Validation of Risk Prediction Models for Cardiovascular and Kidney Outcomes of Non‐ ST‐Segment– Elevation Acute Coronary Syndrome in Patients With Chronic Kidney Disease American Heart Association Journals
Moozonian News
news.ycombinator.com• May 16, 2026• 1 min read
LLM models are not ready for orchestrating many agentsHi, i have been experimenting with many coding agents distributed in a tree like heirarchy. here is what i found: 1. current LLM models are not trained for orchestrating or managing multiple agents, they prefer doing the work by themselves instead of outsourcing it. 2. you can prompt them to follow the given heirarchy but it is not perfect, main agent will stop the sub agent and perform the work by itself. 3. claude swarms was a feature, but it just does not work seamlessly, i don't want 10 agents to give me a single PR, i want a merged final PR, but you have to ask the main agent to give that, and most of the times, the main agent messes things up while merging all the PRs.and the only solution is to include these things in the training of the models, current models are individual workers, and not managers. what was your experience with swarms?
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Moozonian News
news.ycombinator.com• May 11, 2026• 1 min read
Copilot "auto-pilot" system instructions making models worstI use copilot for work, and I have this fight with models all the time because the model has an urgency to get things done,Sometimes I need to explain an issue, elaborate on the constraints, ask the model to research what's going on but I have to fight the model NON STOP to not start writing code... the solution is not to make the code pass where it's broken "fixing" all the unit test to now pass where they fail after the "fix"...To provide the context so a good solution is done and taking into consideration EVERYTHING THAT needs to be done I need to "have a talk" with the model so I can present all the things to consider...Today reading the output I got the two pieces below... first... this totally breaks the model for anything serious.. Why are you pushing the model to do something when I want it to do something else? I'M PAYING FOR IT,But now in the middle of this RANT I notice the model mention that the "Auto pilot" mode is actually pressuring it to assume things and move on to com
Moozonian News
bing.com• May 2, 2026• 1 min read
Q&A: What AI actually does in diffusion models for drug designIn the search for new drugs, artificial intelligence in the form of diffusion models is being used in drug design. What exactly does AI do in this context? Dr. Andrea Mastropietro and Prof. Dr. Jürgen ...
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Moozonian News
news.ycombinator.com• May 1, 2026• 1 min read
Ask HN: Bedrock,Vertex AI etc. Support Anthropic ModelsIs the mechanism more like a smart proxy or do these cloud providers host and serve Anthropic models? if latter, how do they work ? How does Anthropic protect model weights ?
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Moozonian News
github.com• Apr 21, 2026• 1 min read
Show HN: I built a coding agent that works with 8k context local modelsMost AI coding agents assume you have a 200k-context model. In reality, the local models most people actually use have 8k windows — barely enough for one large file, let alone a whole project.This tool works in three steps:-Map: on init, it writes plain Markdown context files: one project-level overview, one per folder, plus a line range index for any file over 150 lines.-Plan: one LLM call reads the map and turns your request into a task list, with dependencies.-Execute: it gives only one file to an LLM call. A token counter checks before every single call, and falls back to loading just the relevant line range if the file is too big.Works with Ollama, LM Studio, Groq, OpenRouter, Gemini, DeepSeek, or any OpenAI-compatible endpoint. Local models run sequentially by default, while cloud providers run in parallel.The hardest part was conversation memory. 8k isn't enough for the full history, and i have tried compression, but it wasn't going to cut it either. The fix was a ring-buffer ev
Moozonian News
fittohire.com• Apr 21, 2026• 1 min read
Show HN: We replaced 5 ML models with 1 shared encoder on an $11/month VPSWe were running 5 fine-tuned MiniLM models for a resume-to-job matching pipeline — 455MB on an $11/month VPS, one per task. I consolidated them into a single shared encoder with 5 lightweight heads: 25MB total, same latency, zero API calls. The matching score went up, not down.The 5 tasks:1. Classify JD lines (requirement vs boilerplate) 2. Split requirements into required vs preferred 3. Disambiguate skill mentions (Python-the-language vs Python-the-ecosystem) 4. Textual entailment (does resume experience satisfy a requirement?) 5. Semantic embeddings for similarity searchAll five share MiniLM-L6 (22M params). Before: 5 x ~91MB fine-tuned copies — essentially the same encoder with different weights, burning RAM for no reason on a 4 vCPU / 8GB box. The obvious idea: share the encoder. One copy in memory, five lightweight heads (~580KB each) routing its output to task-specific predictions.Attempt 1: Frozen encoder, linear heads. Cache CLS embeddings, train heads on cached vectors. Fast.