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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 - alehlopeh/hallu: This web app does not exist
alehlopeh · 2026-06-24 · via Hacker News - Newest: "LLM"

Hallupedia demo   Chatty demo

Hallu is a web framework where an LLM hallucinates your entire app.

With Hallu, you've already shipped features you never even thought of. It's an agent harness that takes a request and acts like a web app against a real database. It's the "interpreter" to Claude Code's "compiler": Application logic is generated at runtime instead of at build time. Think per-request (proofless) program synthesis against an informal prose spec.

Every request routes to a model with a SQL tool and instructions to return html. The model is the controller, the ORM, and the template engine.

Point one of the examples to Haiku and click around to watch a working app assemble itself one feature at a time.

Don't just have Claude write your app. Have Claude be your app.

How it works

Runs on Bun + Hono. Uses SQLite by default, supports Postgres. Bring your own model.

One catch-all route

GET /* asks the model for the page body. A client runtime intercepts form submits and POSTs them to /__hallu/action.

One tool

The model reads and writes the database through a single sql tool in a loop.

Wire format

Actions stream back <hallu-update target="id">...</hallu-update> blocks. The client swaps each region in by id. It feels like an SPA.

Streaming

With streamResponses, an action streams into a container through a stream tool. The framework appends your wrapper markup, fills it as tokens arrive, and fires a hallu:finalize event at the end. Two modes: literal text by default (markup escaped), or html: true to render the stream as live HTML as the tokens land.

Page chat

With pageChat, a floating panel lets the user revise the current page by instruction. The framework posts the page and instruction to /__hallu/revise and the model rewrites it in place. The edit is saved by path glob and re-applied on every later render of a matching page, so it sticks.

Caching

Rendered pages are cached per path so warm loads skip the model. A DB write invalidates affected pages (coarse by default, or scoped with a glob).

Two schema modes

Fixed (tables is the whole schema) or autoSchema (the model creates tables on the fly for paths it hasn't seen)

autoSchema mode

The schema grows as you browse. Visit a path for the first time and the model will design a table, create it, and render the page to html. Add /new to the path, and the model will generate a form. Submit the form and the model will insert the record and render the page. Add /delete to the path, and the model will present you with an "Are you sure?" form and then delete the record, as requested.

tables mode

Pin the schema yourself. Declare your tables in the config and the framework creates them on boot. The model reads and writes only within them and never runs DDL, so the shape of your data is fixed and known.

Examples

NeuroMUD demo

NeuroMUD

A Neuromancer-themed MUD.

Hallupedia

A "real world" encyclopedia. Every article is generated on-demand from the model's knowledge graph, and the same model generates the SQL and html.

Slop Overflow

A programmer Q&A site on Postgres, hybrid autoSchema and declared tables.

Chatty

A ChatGPT-style chat app on streamResponses. The model streams html token-by-token into the thread.

Salesfarce

A schema-less CRM. Visit an object and the model designs and creates its table and seeds it.

Shamazon

A store where the whole catalog is invented on demand: products, prices, reviews, then saved so the page is stable on return.

Get started

Requires Bun.

bunx hallujs generate myapp   # defaults: Anthropic + SQLite
cd myapp && bun install
echo "ANTHROPIC_API_KEY=sk-ant-..." > .env
bun dev

generate takes flags for the backend and provider: --postgres, and --anthropic (default) / --openai / --ollama.

Vibes

Vibing on a Hallu app is kind of like vibe coding through a game of telephone. Use bun quality hallu.config.ts to sanity check your vibed vibes.

Configuration

hallu.config.ts default-exports defineConfig({...}). Required: name, description, model.

Option Type Default
name string App name; document <title>.
description string The domain, data, and rules in prose.
model LanguageModel Any AI SDK model, e.g. anthropic("claude-opus-4-8").
providerOptions ProviderOptions Provider-specific request options forwarded on every call, keyed by provider id (e.g. reasoning/thinking).
streamResponses { container, wrapper, html? } Let an action stream text token-by-token into container, wrapped in wrapper. html: true renders the stream as live HTML instead of literal text. Fires hallu:finalize when done.
tools (ctx) => ToolSet Extra model tools alongside the built-in sql/stream. A factory per request; a tool's execute reads/writes via ctx.sql.
tables Record<string, Record<string, string>> {} Schema as { table: { column: "<sql type>" } }, created on boot.
autoSchema boolean false Let the model CREATE tables/FKs on the fly; else DDL is forbidden.
addFields boolean false "Add field" control that runs ALTER TABLE ADD COLUMN.
pageChat boolean false Floating panel to revise the current page by instruction; the model rewrites it in place and the edit is cached.
design string CSS guidance for the model (class names, Tailwind, ...).
head string Raw HTML injected into <head>.
static string Directory served at web root.
busyIndicator boolean true Use the default loading CSS (top progress bar + content dim) during an action or style hallu-busy/hallu-patched yourself.
navLinks boolean false Have the model render a nav menu of related links.
indexPrompt string Extra render instructions for / only.
routes string[] allow all Allowed path globs (*/**). If set, non-matching paths get a 404
cacheHtml boolean true Serve cached page HTML.
cacheTemplate boolean false Cache a per-shape template, re-render against live data. Overrides cacheHtml.
invalidateOnWrite string[] drop all Globs of pages to drop on write; else every page drops.
identify (c) => Tenant | null single-tenant Resolve the account for a request; DB + cache key per account.
loginPath string 401 Redirect path when identify returns null.
configure (app: Hono) => void Mount your own routes before the catch-all.
temperature number 0.35 Sampling temperature.
maxSteps number 8 Max tool-loop steps per render/action before the framework gives up.
port number env PORT / 7777 Listen port
logLevel "debug" | "info" | "silent" "debug" debug logs every SQL statement.
onSql (e: SqlEvent) => void Called after each sql execution.
database sqlite or postgres sqlite { driver: "sqlite", path?, dir? } or { driver: "postgres", url, schema? }.
seed (db) => void Run once when a DB/schema is first created.
afterWrite (db, events) => void Called after every write

Tenant is { account, context? }

SqlEvent is { query, ok, mutated, error? }

SQLite path defaults to ./hallu.db (per-account files under dir, ./data)

Postgres schema defaults to public.

Caveats

  • It's slow: A page is a model call, sometimes several. Pages take ~2s to load on Haiku. It's a website powered by a tool calling LLM though. What'd you expect?
  • It costs tokens: Every cold request hits the model. There's a cache, so it's not insane, and Haiku is relatively cheap and does the job perfectly.
  • It's non-deterministic: The same URL renders slightly differently on every cold load and cache invalidation.
  • The security model isn't perfect: The framework passes untrusted input to an LLM and asks it to write arbitrary SQL. That's dangerous. There's a script in the repo that tests common SQL-injection techniques. But yeah, it's an LLM hallucinating SQL. Don't use this for anything important.