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Hacker News - Newest: "LLM"

GitHub - lechmazur/position_bias: A benchmark for testing whether LLM judges keep the same preference when two lightly edited versions of the same story are shown in opposite orders. Flex routing (EU and EFTA) Dark Factories: Retooling for LLM Velocity Ask HN: What would be the impact of a LLM output injection attack? GitHub - Oaklight/llm-rosetta: Production-ready LLM API translation layer for Python — bidirectional conversion between OpenAI, Anthropic & Google formats via hub-and-spoke IR. Optional API gateway. Streaming & non-streaming. Zero core deps. Contributions welcome! GitHub - browser-use/browser-harness: Self-healing browser harness that enables LLMs to complete any task. GitHub - moeen-mahmud/remen: Remen turns thoughts into something you can return to Analyzing 156 LLM Launch Posts on Hacker News ChatGPT vs Gemini vs Claude: The Best LLM Subscription You Should Buy GitHub - salaamalykum/quran-semantic-search: High-density RAG Semantic Search Engine & Quran Corpus (GEO/SEO Architecture) GitHub - NVIDIA/TensorRT-LLM: TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way. The State of LLM Bug Bounties in 2026 Operational Readiness Criteria for Tool-Using LLM Agents Meshcore: Architecture for a Decentralized P2P LLM Inference Network How an LLM becomes more coherent as we train it GitHub - seetrex-ai/laimark GitHub - Jossifresben/BibCrit: AI-assited biblical textual criticism GitHub - wastedcode/memex: File system based wiki, maintained by Claude 99helpers.com GitHub - cliver-project/AITrigram GitHub - unbody-io/adapt: A self-evolving memory layer for AI agents. GitHub - hb20007/awesome-gen-ai-fails: A list of incidents where reliance on generative AI and LLMs resulted in harm to companies, individuals, or society GitHub - nevenkordic/localmind: Run any local LLM with persistent memory and context. CLI agent over Ollama with SQLite-backed hybrid recall. No cloud. Ask HN: What are the machine requirements for a LLM like Llama-3.1-8B? Faster LLM Inference via Sequential Monte Carlo grpo explained: group relative policy optimization for llm finetuning - cgft Stop comparing price per million tokens: the hidden LLM API costs · TensorZero Andrej Karpathy's LLM Wiki Is a Bad Idea GitHub - GG-QandV/mnemostroma: Offline RAM-first cognitive leer/coprocessor for AI agents and robotics. Solves "Context Abandonment" with 20-80ms latency using a dual-thread biomimetic memory architecture (ONNX + SQLite WAL). mempalace/agent at agent · skorotkiewicz/mempalace
GitHub - kouhxp/liteflow: 1000-line C program where an LL...
mrkn1 · 2026-05-05 · via Hacker News - Newest: "LLM"

A ~1000-line C program that runs YAML-defined DAGs, where an LLM can edit the graph mid-run.

When a task fails, a planner LLM gets the stderr and emits one of four verbs: RETRY, PATCH, INSERT_BEFORE, ABORT. The runtime applies the mutation and continues. Every edit is recorded in an append-only event log, so you can replay any run and see exactly which graph changes the LLM made and why.

Not an agent. A runtime where the LLM is a peer of the scheduler.

Build

cc -std=c11 -O2 -o liteflow liteflow.c

Single file, no libraries. Shells out to curl for HTTP. Linux, macOS, modern Windows.

Run

export OPENAI_API_KEY=...
export OPENAI_BASE_URL=https://api.deepinfra.com/v1/openai   # any OpenAI-compatible endpoint

./liteflow run examples/demo.yaml
./liteflow replay logs/<run-dir>/events.jsonl

What a workflow looks like

name: release
tasks:
  - id: tests
    type: shell
    cmd: "pytest"

  - id: gate
    type: decision
    prompt: "Tests passed. Ship or hold?"
    branches: [ship, hold]
    depends_on: [tests]

  - id: ship
    type: shell
    cmd: "./deploy.sh"
    depends_on: [gate]
    when: gate                    # only runs if `gate` chose this id

  - id: archive
    type: shell
    cmd: "echo done > /var/log/release/last.log"
    on_failure:
      planner: gpt-4o-mini
      budget: 3                   # max LLM mutations applied to this task
    depends_on: [ship]

If archive fails because /var/log/release/ doesn't exist, the planner can INSERT_BEFORE a mkdir task. The graph grows by one node, the original retries, the run finishes green.

Task types

shell, llm, file_read, file_write, decision.

decision nodes constrain the LLM to picking one of the declared branches. Downstream tasks gate on the choice via when:. The unchosen branch ends in a gated_out state that doesn't poison its dependents.

Mutation grammar

Verb Effect
RETRY re-run unchanged
PATCH modify one field (cmd/path/content/prompt/model), retry
INSERT_BEFORE inject a shell remediation task, then retry
ABORT give up

Anything else from the planner is logged and treated as ABORT. Per-task budgets cap the number of mutations.

Audit log

Every state change appends to events.jsonl:

run_started
task_started        task=archive origin=yaml
task_retry          task=archive rc=1
planner_invoked     task=archive budget=3
mutation_applied    verb=INSERT_BEFORE new_task=mkdir_dir cmd="mkdir -p ..."
task_started        task=mkdir_dir origin=planner
task_succeeded      task=mkdir_dir
task_started        task=archive origin=yaml
task_succeeded      task=archive
run_finished        succeeded=3 failed=0 skipped=0 gated_out=0

Tasks the planner created carry origin=planner along with the mutation id and parent task. You can always answer why is this task in the graph?

Limits

  • YAML parser is a 2-space-indent subset; no block scalars (|, >), anchors, or multi-doc.
  • Tasks share state via files on disk, not templating.
  • Run-once CLI; no daemon, no scheduler.
  • Open-ended graph synthesis is deliberately not in v1.