OptiLLM: The Drop-In Proxy That Makes Any LLM Reason Better — No Retraining Required
An open-source, OpenAI API-compatible proxy that wraps LLM calls with 20+ inference-time optimization techniques — chain
56pages
Published and in review
Diff pages in this prototype
GPT-5.6 Public Launch Today: Sol, Terra, Luna Family Revealed
OpenAI releases GPT-5.6 in three tiers — Sol, Terra, Luna — with benchmark scores up to 91.9%, pricing from $1/M tokens,
Anthropic Raises $65 Billion in Series H at $965 Billion Valuation — Largest AI Funding Round, Surpasses OpenAI
On May 28, 2026, Anthropic closed a $65 billion Series H financing round, achieving a $965 billion post-money valuation
Claude Sonnet 5 vs GPT-5.5: Benchmarks, Pricing, and the Mid-Tier Sweep
Claude Sonnet 5 beats GPT-5.5 across every directly comparable benchmark while costing 40–50% less. Here's the full brea
Gemini 3.5 Pro Cleared for July Launch While Fable 5 and GPT-5.6 Remain Restricted
Google's Gemini 3.5 Pro ships freely while competitors face regulatory blocks — the first real-world case of AI regulato
Deploying Distributed AI Inference: Blueprints & Troubleshooting
Seven practical deployment blueprints for distributed AI inference on vLLM, llm-d, and Red Hat OpenShift AI — matched to
Llama 4 vs Qwen 3.5 vs Mistral Large 3: The Best Open-Weight LLM in July 2026
A head-to-head comparison of the three strongest open-weight LLM families — Llama 4 Scout, Qwen 3.5/3.6, and Mistral Lar
NVMe KV Cache Offloading for LLM Inference: Serve 10x More Users on One H100
Turn fast local storage into a third tier alongside GPU memory — serving 10x more concurrent users with 2x throughput an
Mistral AI Unveils New 'Fat' Open-Weight Model Family — June Tease Becomes July Early Access
Mistral AI's new fat open-weight model family expands on Large 3's MoE design, targeting Anthropic's Sonnet 5 with more
Claude Sonnet 5 Is Now the Default for Free and Pro — What the Model Shift Means
Claude Sonnet 5 is Anthropic's default for Free and Pro users — near-Opus performance, agentic free-tier access, and a n
Claude Sonnet 5 vs Opus 4.8: Agentic Pricing and Cost-Performance Tradeoffs
Claude Sonnet 5 delivers 80–90% of Opus 4.8's agentic coding quality at 20–40% of the cost. Here's how to choose, combin
OpenAI Retires GPT-4.5 and o3: What Developers Need to Know
OpenAI is retiring GPT-4.5 and o3 from ChatGPT and the API, accelerating its model lifecycle from 18 months to 6. Here's
Claude Sonnet 5 Tokenizer Benchmark: Token Count, Cost, and Performance Impact
Sonnet 5's new tokenizer produces 30–42% more tokens at the same per-token price — here's what the benchmarks, cost audi
OpenAI Retires Version Numbers for Sol, Terra, and Luna Tier Naming System
OpenAI abandons traditional model versioning in favor of durable capability tiers — Sol, Terra, and Luna — under GPT-5.6
Google Unveils Open Knowledge Format v0.1 — The Missing Standard for AI Agent Knowledge
Google Cloud published OKF v0.1, an open, vendor-neutral specification for representing knowledge that AI agents can rea
Anthropic Gets Partial Mythos 5 Access After $20M Compliance Overhaul — The Selective Restoration That Redefines Frontier AI Access
Fifteen days after the US Commerce Department ordered Anthropic to suspend Claude Mythos 5 and Fable 5 globally, the gov
OpenAI's Confidential IPO S-1: $25B Revenue, But Losing $1.22 Per Dollar Earned
OpenAI confidentially filed its S-1 with the SEC on May 22, 2026, targeting a September listing above $1 trillion. The f
OpenAI Shifts to Three-Tier Pricing with GPT-5.6 — Named Tiers, Government-Only Limited Preview, and Ultra Subagent Mode
On June 26, 2026, OpenAI previewed GPT-5.6 in a bold new strategy: instead of a single model with a 'reasoning strength
Microsoft Launches Seven MAI Models at Build 2026 — Including First Proprietary Reasoning Model
At Microsoft Build 2026, Microsoft AI unveiled its first in-house MAI model family spanning seven new models across reas
US Bans Claude Fable 5 Worldwide — What the Export Control Precedent Means for Every AI Startup
On June 12, 2026, the US Department of Commerce issued an emergency export control directive ordering Anthropic to immed
Google DeepMind's Exodus: Nobel Laureate Jumper, Gemini Co-Lead Shazeer Join Rivals in Mass Departure
Google DeepMind is facing its worst talent drain in recent history. Over a single week ending June 19, four senior resea
OpenWeave 7B Releases With Native Multimodal Reasoning and Full Apache 2.0 Licensing
The OpenWeave consortium released its first production-ready open-weight multimodal model featuring native vision-langua
Micron Confirms HBM Memory Shortage Will Outlast 2027 — The Hidden Bottleneck in AI Compute
Micron's latest earnings confirm HBM shortages won't resolve by end of 2027 despite massive capex increases — the bottle
EU AI Office Mandates Machine-Readable Watermarking for All Commercial LLM Outputs
The EU's Phase II mandate forces every commercial LLM provider to embed cryptographically verifiable watermarks or face
Building an agent harness for local LLM evaluation
A practical guide to designing, running, and scoring local LLM agent evaluations offline using Ollama, LangChain, and cu
Edge AI inference: Running LLMs on Raspberry Pi 5 and Jetson with quantization
A practical guide to deploying quantized LLMs on Raspberry Pi 5 and NVIDIA Jetson, covering benchmarks, runtime tools, a
Multi-modal LLMs for production: when to use image/audio/video capabilities
Learn how to decide between text-only and multi-modal LLM workflows by evaluating latency, cost, and accuracy requiremen
GLM-5.2: Open-weights model beats GPT-5.5 for 1/6th the cost
Z.ai's GLM-5.2 outperforms GPT-5.5 on SWE-bench Pro while offering a massive 1M context window and significantly lower i
Anthropic restricts access to Claude Fable 5 and Mythos 5 for foreign nationals following US export order
Learn how a US Commerce Department directive under the Defense Production Act forced Anthropic to disable its most advan
LLMs for legal and compliance teams: contract analysis, e-discovery, and regulatory monitoring
Practical guide for using LLMs in legal and compliance workflows — contract clause extraction, privilege-aware e-discove
LLMs in regulated industries: compliance infrastructure and deployment patterns
Practical guide to deploying LLMs under HIPAA, SOC2, FCA and GDPR — including audit infrastructure, guardrails, output l
Hosting LLMs at scale: vLLM, TGI, SGLang and TensorRT-LLM compared
A performance-focused comparison of production inference engines: vLLM, SGLang, TensorRT-LLM, and TGI. Covers throughput
GPU rental vs API pricing: when to self-host
Break-even framework for GPU rental vs API pricing: when does renting a GPU beat paying per token?
LLM agent frameworks compared: LangChain, CrewAI, AutoGen, OpenAI Agents SDK
Comparing LangChain, CrewAI, AutoGen, and OpenAI Agents SDK on architecture, production readiness, observability, and wh
Quantisation explained: why model files have Q4, Q5 and GGUF labels
A plain-English guide to LLM quantisation: what Q4, Q5, Q8 and GGUF labels mean, how quantisation affects quality and sp
Changelog watching for AI teams: deprecations, pricing and model aliases
A monthly review checklist for keeping AI applications stable amid provider changes: model deprecations, pricing updates
What model cards tell you — and what they do not
How to read an LLM model card critically: what the claims mean, what is often missing, and how to spot the important gap
UK AI governance sources: ICO, NCSC, CMA and DSIT in one map
A practical guide to UK AI regulation sources — which UK regulator handles what, and where to find the official guidance
Small language models: when smaller is better
A practical guide to choosing small language models for latency-sensitive, cost-constrained or on-device AI tasks, with
Responsible AI policies that builders can actually operationalise
A practical guide to turning abstract responsible AI principles into release gates, evaluation checklists, incident revi
Reasoning models: what thinking modes change for cost and latency
A plain-English explanation of reasoning models, extended-thinking modes, and when the extra cost and latency are worth
Provider data retention policies: what API users should compare
A neutral checklist for comparing how AI API providers handle your data: training use, retention periods, abuse monitori
OpenAI, Anthropic, Google and Mistral APIs: what comparison pages should measure
A neutral comparison rubric for evaluating hosted LLM providers: what to compare, what to ignore, and how to avoid misle
NIST AI RMF and GenAI guidance: practical use for small teams
How to apply NIST's AI Risk Management Framework and GenAI profile as a lightweight launch checklist — without enterpris
Mixture-of-experts models: why active parameters matter
A plain-English explanation of mixture-of-experts architecture: why model labels list total and active parameters separa
Meta Llama and open model licensing: what builders must check
A practical guide to open-model licensing for builders: what Llama, Apache 2.0, and other open-weight licenses actually
Local LLM runtimes: Ollama, llama.cpp, vLLM and TGI in plain English
A practical comparison of the four main open-source LLM runtimes: when to use Ollama for prototyping, llama.cpp for sing
Hardware supply and inference economics: why chips shape AI products
A practical guide to how GPU and accelerator supply affects AI product cost, availability, and design decisions from tra
Guardrails compared: policy prompts, classifiers, validators and permissions
A practical comparison of LLM safety guardrails: where policy prompts, input and output classifiers, validators, and per
EU AI Act for LLM buyers: what to track without overclaiming
A practical map of EU AI Act obligations for teams buying and using LLM APIs — separating GPAI provider duties from down
Enterprise AI procurement: questions before buying a platform
A practical procurement checklist for comparing AI platforms, checking data control, security, model choice, cost and ex
Copyright and training data: what AI product teams can responsibly say
A plain-English guide to the three-layer copyright question in AI: training data use, output similarity, and what produc
AI vendor lock-in: model APIs, embeddings, vector stores and eval data
A practical guide to understanding and mitigating AI vendor lock-in across model APIs, embeddings, vector stores, prompt
AI energy use: useful facts without moral panic
A balanced guide to AI energy consumption: separating training, inference, datacentre, and hardware manufacturing energy
AI adoption in small businesses: where LLMs help first
A practical ranking of LLM use cases for small businesses: where the return on AI investment is clearest, where the risk
AI SLAs and status pages: what reliability evidence vendors publish
How to evaluate AI API reliability claims: what SLAs actually cover, how to read status pages critically, and what relia
What counts
A useful Diff page answers "so what?"
- What changed: model, API, pricing, policy, benchmark, availability, tooling, or risk.
- Who should care: builders, buyers, researchers, operators, regulators, or users.
- What remains unproved: benchmarks, reliability, pricing edge cases, uptime, safety, or real workload fit.
- What to do next: ignore, monitor, test, migrate, budget, update policy, or brief stakeholders.