Pi 0.85.0
New version of pi. Download from npm, view release on GitHub, or read release notes.
Pi Coding Agent Blog3 min read
vLLM
267 stories
From MIT to IBM, expediting AI and quantum deployment
MIT affiliates engage with the MIT-IBM Computing Research Lab to bring rigorous theory to production systems.
MIT News - AI6 min read
Granite 4.2 LLMs: How They're Built
TL;DR: Granite 4.2 is our first family of dense, decoder-only reasoning LLMs, released in three sizes: 3B, 8B, and 30B. These models are post-trained from Granite-4.1 base models.
Hugging Face Blog21 min read
How Top VC Firms Evaluate Founders: The Taste Matrix of Sequoia, Benchmark, a16z, Founders Fund, Thrive, and Lightspeed
Data from ~70 seed rounds (2024-2026) shows how Sequoia, Benchmark, a16z, Founders Fund, Thrive, and Lightspeed pick founders. Six personas reveal your best-fit firm.
Vela Partners Blog16 min readPi 0.84.3
New version of pi. Download from npm, view release on GitHub, or read release notes.
Pi Coding Agent Blog6 min read
Qwen3.8-Max open weights: 4.89 TB, 224 files, one license catch
Alibaba finally opened a Max-class Qwen — and the 2.4T checkpoint downloads at 4.89 TB, three times Kimi K3 for a smaller model, because Qwen shipped BF16 where Moonshot shipped pre-quantized 4-bit.
Backgrind Blog13 min read
How Hugging Face Inference Endpoints, Jobs, and Buckets Power Search on Papers with Code
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Hugging Face Blog11 min readStop managing infrastructure: A new way to deploy AI agents and models
Standing up an agent as a production service on Kubernetes means five YAML files, a few hundred lines between them, and (in most enterprises) a ticket in someone else’s queue. On the Workload API it means one spec file, one command, and about five minutes to a live URL.
DataRobot Blog8 min read
Self-Hosting an AI-Native CRM in 2026
Self-host Relaticle with Docker, Coolify, or Dokploy. Understand the additional model and broadcasting setup needed for chat, plus MCP and credit limits.
Relaticle Blog9 min read
Teaching NVIDIA Nemotron 3.5 Lightning to route code reviews
CodeRabbit worked with NVIDIA and Baseten to post-train NVIDIA Nemotron 3.5 Lightning for one of CodeRabbit's highest-volume routing tasks, improving route agreement while reducing estimated inference cost.
CodeRabbit Blog5 min read
Release: v5.15.0
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training. - Release Release: v5.15.0 · huggingface/transformers
Transformers20 min readPi 0.84.0
New version of pi. Download from npm, view release on GitHub, or read release notes.
Pi Coding Agent Blog11 min read
Lossless Inference
How to make LLM serving faster without touching the model. Exact kernels, speculative decoding, lossless compression, KV reuse and scheduling, with the math and how to verify it with logit parity.
RunInfra Blog8 min read
Data Tables, JSON Condition V2 & Faster AI Workflows
L'articolo Data Tables, JSON Condition V2 & Faster AI Workflows proviene da Tiledesk.
Tiledesk Blog5 min read
$0.09 and $290.12: What Actually Moves Your Inference Bill
Nine cents and $290.12 are both prices for one million output tokens. The gap is driven by accelerator, provider, concurrency, utilization, and reasoning token volume.
RunInfra Blog10 min read
Under the Hood: Serving Kimi K3
How DigitalOcean served Kimi K3 on day 0: GPU selection (B300/MI350X), vLLM tuning, and the OpenAI-compatibility fixes for tool calling, streaming, and reasoning.
DigitalOcean Blog14 min read
Serving Kimi K3 on vLLM was hard. Here is what we measured.
No released vLLM build served Kimi K3 when we started. We resolved several distinct failures on 8x B300 and measured the result, its operating crossover, and its honest limits.
RunInfra Blog10 min read
ThunderAgent: 2x Faster Agentic Inference for Synthetic Data Generation at Scale
ThunderAgent is a program-aware scheduler for agentic inference. By treating each agent workflow as a schedulable program, it eliminates KV cache thrashing to deliver more than 2x single-node throughput and near-linear multi-node scaling.
Together AI Blog6 min read
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