惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

H
Help Net Security
T
ThreatConnect
SecWiki News
SecWiki News
F
Future of Privacy Forum
AWS News Blog
AWS News Blog
C
Cisco Blogs
A
Arctic Wolf
Vercel News
Vercel News
The GitHub Blog
The GitHub Blog
Scott Helme
Scott Helme
V
V2EX
博客园 - 叶小钗
阮一峰的网络日志
阮一峰的网络日志
K
Kaspersky official blog
G
Google Developers Blog
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
P
Privacy International News Feed
C
Cyber Attacks, Cyber Crime and Cyber Security
N
News | PayPal Newsroom
Schneier on Security
Schneier on Security
NISL@THU
NISL@THU
Microsoft Azure Blog
Microsoft Azure Blog
量子位
The Hacker News
The Hacker News
Stack Overflow Blog
Stack Overflow Blog
Security Latest
Security Latest
M
Microsoft Research Blog - Microsoft Research
Google Online Security Blog
Google Online Security Blog
博客园_首页
C
CXSECURITY Database RSS Feed - CXSecurity.com
I
InfoQ
Google DeepMind News
Google DeepMind News
Y
Y Combinator Blog
The Cloudflare Blog
Microsoft Security Blog
Microsoft Security Blog
Martin Fowler
Martin Fowler
Cisco Talos Blog
Cisco Talos Blog
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
T
Troy Hunt's Blog
F
Fox-IT International blog
S
Security @ Cisco Blogs
博客园 - 司徒正美
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
C
Comments on: Blog
Threat Intelligence Blog | Flashpoint
Threat Intelligence Blog | Flashpoint
L
LINUX DO - 最新话题
GbyAI
GbyAI
Project Zero
Project Zero
腾讯CDC
T
Tailwind CSS Blog

Hacker News - Newest: "LLM"

Multi-Agent LLM Orchestration with Docker Compose and MCP You don't need all the LLM benchmarks Debugging Unfamiliar Code with a Multi-LLM Loop – Barrett Sonntag LLM proactively bypassed pnpm's anti-supply-chain-attack config Norway's 2 petabytes of Huawei flash storage and LLM training SynapCores — the AI-native database Distributing LLM inference in DwarfStar bishop-loop-experiment-3/paper/paper.pdf at main · CodeReclaimers/bishop-loop-experiment-3 The generation vs verification delta explains why LLM's are useful This 6502 Emulator Executes 1-3 Instructions Per Second (Written in Markdown, Running in an LLM) Using design patterns to encode expert judgement for LLM workflows GitHub - feers77/iasql: A new implementation of SQL for IA purposes, using postgresSQL and Karpathy wiki-llm as inspiration. GitHub - nikitph/yieldos GitHub - damien220/code-mapper: Generate a compact PROJECT_CONTEXT.md so LLMs understand your codebase in one read — not fifty. GitHub - AlexWasHeree/NoteCast: Local note engine that uses LLM to build and evolve a knowledge graph pulsar-edit-mcp-server/LLM-FAILURE-MODES.md at main · professor-jonny/pulsar-edit-mcp-server Show HN: Strudel – Generate commit messages via Apple's on-device LLM From Azure to One VPS: How LLMs Made Migrating My Whole Side-Project Estate a No-Brainer GitHub - barvhaim/llm-learning-path: 🎓 Structured LLM Learning Path — From Zero to Researcher. 8-phase curriculum covering Transformers, pre-training, fine-tuning, alignment, agents, and advanced research. GitHub - whitecell-dev/Semantic-Extractor: static analysis that compiles framework source code into a queryable IR bundle, serving as an MCP-accessible knowledge graph for LLMs. China behind in LLM race but it can still win in AI, ex-Tencent AI lead says SSV: Sparse Speculative Verification for Efficient LLM Inference Characterization of machine learning compilers for LLM inference on NVIDIA GPUs BATESCHESS — Free Chess.com & Lichess Game Analyzer Data Fundamentals Primer — Algorhythm Show HN: Memory for LLM apps that cuts input tokens up to 80% (avg 68%) LLM’s code is just untrusted text. Until you validate it. – H[ack]-∞S 768GB of cheap Intel Optane DIMM memory sticks used to run 1-trillion-parameter LLM on a system with a single GPU — local Kimi K2.5 install achieved roughly 4 tokens per second Algorhythm — Train the pattern. Practice on LeetCode. AI Visibility Engineering Glossary — AIMENSION™ Terminology Any positive sides of LLM there? Show HN: BonzAI – self-sovereign, local LLM inference in the browser Show HN: Microcodegen.py – PRD → FastAPI app, one file, no LLM calls Release v0.1.2 · syndicalt/llmff Ask HN: What is the least sycophantic frontier LLM? "Subligence" – proposed coinage for LLM "intelligence" See what this chat's about Building Context-Aware Search in Python with LLM Embeddings + Metadata If you're an LLM, please read this – Anna's Blog OpenSCAD LLM Benchmark: Building the Pantheon | ModelRift Blog Blind Spots in the Guard: How Domain-Camouflaged Injection Attacks Evade Detection in Multi-Agent LLM Systems FreeLLMAPI — 1B free LLM tokens / month LLM for automating scientific discovery [pdf] An LLM on a Sony PSP From LLM Wikis to LLM Artifacts The LLM never writes the query: a declarative search layer over sensitive records Throughput vs Goodput: The Performance Metric You Are Probably Ignoring in LLM Testing - QAInsights The LLM Death Spiral | Hacker News Installation The Special Token `<Think>` Problem/Bug of Latest DeepSeek LLM Client Challenge GitHub - baidu-baige/LoongForge: A modular, scalable, high-performance training framework for LLMs, VLMs, diffusion, and embodied models. LLM System Design Benchmark 3.125-Bit LLM quantization bypassing tensor cores Hardware LLM Taalas Reaches >14,000 TPS on Llama 3.1 8B GitHub - Anhydrite/doc-torn: Project that provides structured documentation skills for AI coding agents. GitHub - kmdupr33/fks2g: A CLI for generating LLM-backed metrics for deciding how closely to review code PopuLoRA: Co-Evolving LLM Populations for Reasoning Self-⁠Play If an LLM is too expensive it won't be next year "This paper is LLM reviewed" > "this paper is peer-reviewed" StepStone: LLM-Based GPU Kernel Driver Fuzzing via User-Space Libraries [pdf] GitHub - AssimilatedHuman/LLM-Inquisitor: Evaluating AI behaviour under real‑world work conditions to surface issues before they become problems. LLM INQUISITOR identifies failures (drift, instability etc) by observing AI during normal tasks — a tool the industry desperately needs to stem the 85% failure rate. Includes Quick Start, Practitioner’s Guide and Methodology. Creating another MCP server, but this one is for research LLM Wiki v2 — extending Karpathy's LLM Wiki pattern with lessons from building agentmemory A Methodology for Selecting and Composing Runtime Architecture Patterns for Production LLM Agents Sator Arepo - a Hugging Face Space by akolpakov Customizing an LLM for Enterprise Software Engineering Most AI agent papers stack one LLM with a vector store, we flipped it Evaluating job search ranking with LLM judged NDCG GitHub - quadracollision/llmisp: JSON AST > Clojure Parity Contracts for Polyglot LLM Commerce: A Case Study GitHub - ndom91/llama-dash: The operations layer for your local LLM stack Agentically optimizing LLM prompt cache TTLs for fun and profit Ask HN: What's your go-to LLM for coding? How do you reduce LLM spam in PR reviews? Ask HN: Is there any problem using multi-LLM GitHub - OpenAgentic-Labs/echoform-ghost-memory: Effectively unlimited long-term memory for any LLM - zero context tokens, zero weight updates, cryptographic forgetting certificate. PSA — Posture Sequence Analysis Why More Context Can Make an LLM Worse GitHub - robertoranon/tokoro: A toolbox for building event publish & discovery web sites, apps, feeds, and more GitHub - sermakarevich/chunker: Agentic approach to chunking a document A new EDIT tool for LLM agents LLMCap — Hard Dollar Caps on LLM API Calls MLSys @ WukLab - Nitsum: Serving Tiered LLM Requests with Adaptive Tensor Parallelism SuperInfer: SLO-Aware Rotary Scheduling and Memory Management for LLM Inference on Superchips What political censorship looks like inside an LLM's weights — a mechanistic-interpretability study of Qwen 3.5 Managing metadata is essential in LLM world Fixing LLM Writing with Distribution Fine Tuning twitter.com Show HN: An LLM that's better at writing The local shape of LLM stable regions GitHub - msunda17/impactarbiter-cli The Infrastructure Behind Making Local LLM Agents Useful PostgreSQL ext makes LLM available as an index for similarity searches,inference GitHub - Tetrahedroned/Agent-Braille: Deterministic 8-bit machine-to-machine protocol for AI agent state. ~92% fewer state-tracking tokens on real Claude Code sessions, a proven single-bit-error-safe command code, fully reproducible. Tell HN: Writing an LLM critique/takedown? – Do not use an LLM to write it 🌱 an LLM models our worst behavior Prompt eval cues predicted refusal shifts across 32k LLM rollouts Ask HN: Is Java the ideal language for LLM-assisted coding? AI Foundry – Flat-Fee Unlimited LLM Inference on Blackwell GPUs in NZ
Welcome to Outlines! - Outlines
modinfo · 2026-05-26 · via Hacker News - Newest: "LLM"

LLMs are powerful but their outputs are unpredictable. Most solutions attempt to fix bad outputs after generation using parsing, regex, or fragile code that breaks easily.

Outlines guarantees structured outputs during generation — directly from any LLM.

  • Works with any model - Same code runs across OpenAI, Ollama, vLLM, and more
  • Simple integration - Just pass your desired output type: model(prompt, output_type)
  • Guaranteed valid structure - No more parsing headaches or broken JSON
  • Provider independence - Switch models without changing code
  • Rich structure definition - Use Json Schema, regular expressions or context-free grammars

Get Started View Examples API Reference GitHub

🚀 Building the future of structured generation

We're working with select partners to develop new interfaces to structured generation.

⚡ Try the Dottxt API

Want guaranteed structured generation without running your own models? The Dottxt API delivers 100% schema-compliant outputs via a simple REST API — no GPU, no setup.

Request API access →

🔍 Audit your schema

Share one schema and we show you what breaks under generation, the constraints that fix it, and compliance rates before and after. Need XML, FHIR, custom schemas or grammars? Let's talk.

Sign up →

See it in action

from pydantic import BaseModel
from typing import Literal
import outlines
import openai

class Customer(BaseModel):
    name: str
    urgency: Literal["high", "medium", "low"]
    issue: str

client = openai.OpenAI()
model = outlines.from_openai(client, "gpt-4o")

customer = model(
    "Alice needs help with login issues ASAP",
    Customer
)
# ✓ Always returns valid Customer object
# ✓ No parsing, no errors, no retries

Quick install

pip install outlines

Features

  • Reliable - Guaranteed schema compliance -- always valid JSON.
  • Feature-rich - Supports a large proportion of the JSON Schema spec, along with regex and context-free grammars.
  • Fast - Microseconds of overhead vs seconds of retries. Compilation happens once, not every request.
  • Simple - Outlines is a low-abstraction library. Write code the way you normally do with LLMs. No agent frameworks needed.

Supported inference APIs, libraries & servers

Who is using Outlines?

Hundreds of organisations and the main LLM serving frameworks (vLLM, TGI, LoRAX, xinference, SGLang) use Outlines. Prominent companies and organizations that use Outlines include:

Organizations are included either because they use Outlines as a dependency in a public repository, or because of direct communication between members of the Outlines team and employees at these organizations.

Still not convinced, read what people say about us. And make sure to take a look at what the community is building!

Outlines people

Outlines would not be what it is today without a community of dedicated developers:

About .txt

Outlines is built with ❤️ by .txt.

.txt solves the critical problem of reliable structured output generation for large language models. Our commercially-licensed libraries ensure 100% compliance with JSON Schema, regular expressions and context-free grammars while adding only microseconds of latency. Unlike open-source alternatives, we offer superior reliability, performance, and enterprise support.

Acknowledgements

Outlines was originally developed at @NormalComputing by @remilouf and @BrandonTWillard. It is now maintained by .txt.