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52 articles tagged #LLM.
P.01Mistral's CEO confirmed a new open-weight MoE model entering partner early access in July 2026. No specs are public yet, but the real story is what's sold around it: Studio and Forge, a sovereign AI infrastructure play for enterprises wary of depending on a US cloud.
P.02Meta's Muse Spark 1.1 launched July 9 as the company's first metered, pay-as-you-go model, priced at $1.25/$4.25 per million tokens with a 1M-token context window and native subagent orchestration. Here's what it's actually for, and who should skip the waitlist.
P.03xAI and Cursor shipped Grok 4.5 on July 8, trained on trillions of tokens of real Cursor usage data, priced at $2/$6 per million tokens. Here's how it actually compares to GPT-5.6 and Claude Opus 4.8 on price and where it fits your stack.
P.04Sysdig's threat research team documented a ransomware intrusion where an LLM agent handled reconnaissance, credential theft, lateral movement, and extortion end to end, with no human operator directing the individual steps. Here's what changed and what to do about it.
P.05CISA added Langflow's authorization bypass to its Known Exploited Vulnerabilities catalog on July 7, with a federal remediation deadline of July 10. Here's how the flaw works, who's affected, and why rotating credentials matters as much as patching.
P.06LongCat-2.0 is a 1.6-trillion-parameter coding model Meituan trained entirely on Chinese-made chips, no Nvidia involved, then ran anonymously on OpenRouter before open-sourcing it under MIT. Here's what the benchmarks actually show and what it means for teams picking a coding model.
P.07After slipping from a June target, Google's Gemini 3.5 Pro is rolling out to general availability in July 2026 with a 2 million token context window and a gated Deep Think reasoning mode. Here's what actually changes for product teams.
P.08OpenAI is launching GPT-5.6 Sol on Cerebras wafer-scale hardware in July 2026 at up to 750 tokens per second, roughly an order of magnitude faster than typical GPU inference. Here's who that speed is actually for.
P.09OpenAI's GPT-5.6 preview splits into three tiers instead of one model: Sol for frontier work, Terra at half the cost of GPT-5.5, and Luna for high-volume calls. Here's what changed, what it costs, and which tier actually fits your workload.
P.10A command injection flaw in LiteLLM's MCP test endpoints, chained with a Starlette host-header bypass, lets an unauthenticated attacker run arbitrary commands on your AI gateway and steal every provider key behind it. Here's what's affected and how to patch.
P.11MiniMax M3, released June 1 2026, is the first open-weight model to combine frontier-tier coding performance, a 1-million-token context window, and native multimodality in a single model. Here's what it does, how it benchmarks, and why it matters.
P.12AI engineer is not the same job as machine learning engineer. One trains models. The other builds products on top of them. This is the screen that tells them apart and finds candidates who can ship real LLM-powered systems.
P.13LLM API bills grow faster than usage. These are the concrete techniques that cut costs by 40-80% without degrading quality: prompt caching, semantic deduplication, tiered model routing, and batch inference.
P.14Comparing LangSmith, Braintrust, and Weights & Biases Weave for LLM evaluation. What each platform does well, where it breaks down, and how to build a minimum viable eval pipeline.
P.15Both frameworks can build RAG pipelines and agent systems, but they're designed with different priorities. Here's when to reach for each and when to skip both.
P.16Ollama makes it possible to run Llama 3, Mistral, Phi-4, and dozens of other open-weight models on your laptop or server with a single command. Here's what actually works and when local inference makes sense.
P.17Workers AI gives you access to a catalog of open-weight models — Llama, Mistral, Whisper, embedding models — running in Cloudflare's network. Here's what's actually useful, what the limitations are, and when it makes sense.
P.18Getting a language model to return reliably structured data is not just about asking nicely. Here's the pattern that actually works at production scale.
P.19MCP has become the standard for connecting AI models to external systems. Here's how it works, how to implement an MCP server, and what to watch out for before going to production.
P.20Clients ask agencies to 'do something with these PDFs' more often than you'd think. Here's how to actually build document extraction pipelines that work in production: OCR, vision models, and structured output.
P.21Both approaches customize LLM behavior for your use case, but they solve different problems. Here is how to decide which one you need, how to know when to use both, and what teams consistently get wrong.
P.22Three leading agent orchestration frameworks, three different mental models. Here's when each one earns its place, what each costs you in complexity, and what the choice looks like when you're debugging at 2am.
P.23An agent that forgets everything when the session ends is a limited tool. Here are the practical patterns for building different kinds of memory into your agents.
P.24When your AI agent needs to run the code it writes, you can't let it touch your production servers. Here's how the main isolation options work and when to use each.
P.25The Vercel AI SDK has become the default for building AI features in JavaScript apps. Here is what it actually does, how its core primitives work, and where the sharp edges still live.
P.26Hallucination is not a bug that gets patched in the next model release. It is a property of how language models work. Here are the patterns that actually reduce it in production systems, and what they cost.
P.27Single-provider AI dependencies are a reliability risk. Routing layers like LiteLLM and OpenRouter let you fall back across providers, cap costs, and try smaller models first. Here is the architecture and when it actually matters.
P.28Add similarity search to your existing Postgres database using pgvector. Real setup, indexing strategies, and when you actually need a dedicated vector database.
P.29Building an AI feature is only half the work. Once it's in production, you need to know when it's drifting, what it's costing, and where it's failing. Here's how to instrument LLM applications properly.
P.30Unit tests tell you if your code does what you wrote. They don't tell you if your AI feature does what users need. Here's how to build an evaluation pipeline that catches the failures that matter before users do.
P.31LLM calls are slow and expensive. Caching them is the obvious move. But caching the wrong responses breaks the user experience in ways that are subtle and hard to debug. Here's a practical guide to doing it right.
P.32Rolling back a bad API endpoint takes seconds. Rolling back a bad LLM integration is harder — the damage may already be in your logs, your users' inboxes, or your clients' feeds. Feature flags are how you ship AI features without betting everything on launch day.
P.33AI features ship fast. Then the monthly API bill arrives. Here's a systematic approach to understanding and reducing LLM costs without breaking the product.
P.34Getting a language model to return valid, schema-conforming JSON is harder than it looks. Here's what works in production, from native structured output APIs to library-level validation.
P.35Every AI integration budget starts with API costs and ends with surprises. Here's what production AI features actually cost once you account for everything the initial estimate missed.
P.36Prompt injection is the SQL injection of the AI era. As LLMs ship into production apps by the millions, attackers are learning how to hijack them through the data they consume. Here's what the attack looks like and how to defend against it.
P.37Explore ZeroDayBench—A new benchmark testing the efficacy of leading LLM agents in discovering and patching unseen security vulnerabilities.
P.38EditorPickPrompt engineering is dead. Context engineering -- managing system prompts, RAG results, tool outputs, memory, and conversation history -- is the skill that matters now. Here is what changed and why.
P.39A technical deep dive into DeepSeek V4's Engram conditional memory, Manifold-Constrained Hyper-Connections, and Sparse Attention -- the three innovations enabling million-token context at a fraction of the cost. Benchmarks, architecture diagrams, and what it means for your stack.
P.40EditorPickFebruary 2026 saw an unprecedented wave of AI model releases from OpenAI, Anthropic, Google, and others. We break down GPT-5.3 Codex, Claude Opus and Sonnet 4.6, Gemini 3.1 Pro, DeepSeek V4, and every major launch -- with benchmarks, pricing, and practical guidance.
P.41A systematic comparison of modern RAG approaches in 2026: ColBERT, SPLADE, hybrid search, contextual retrieval, and late interaction models. Benchmarks, architecture tradeoffs, and when RAG beats fine-tuning.
P.42A hands-on guide to running Llama 4, Qwen3, Phi-4, and Mistral on consumer GPUs like the RTX 4090 and 5090. Covers quantization formats, inference engines, VRAM needs, and when local beats API calls.
P.43EditorPickClaude Sonnet 4.6 matches Opus performance at Sonnet pricing. Full breakdown of benchmarks, features, adaptive thinking, and what it means for developers.
P.44Stop guessing which AI approach to use. This decision framework with real cost, latency, and accuracy comparisons helps you pick the right one every time.
P.45EditorPickNaive RAG is broken. Here is how contextual retrieval, hybrid search, and intelligent chunking are reshaping how we build AI applications in 2026.
P.46DeepSeek's V4 model brings 1 trillion parameters, Engram conditional memory, and open-source weights under Apache 2.0. We break down the architecture, coding benchmarks, geopolitical implications, and what it means for developers.
P.47EditorPickThe line between web development and AI development has dissolved. The best agencies now ship web apps with built-in intelligence — chatbots, predictive features, automated workflows. Here's what this shift means.
P.48DeepSeek and Alibaba's Qwen surged from 1% to 15% global AI market share in a single year. With 700M+ Hugging Face downloads, open-source AI from China is reshaping enterprise choices, developer workflows, and the competitive landscape.
P.49EditorPickThree approaches to customizing AI for your use case, with cost comparisons, performance benchmarks, implementation timelines, and a decision framework. The guide we wish existed when we started.
P.50Traditional test suites break when outputs are non-deterministic. Here's how we test AI-powered features — from LLM output validation to regression testing for prompt changes, with real frameworks and examples.
P.51EditorPickMulti-agent systems sound great in demos but break in production. Here's how to architect, orchestrate, and monitor AI agent teams that reliably handle complex workflows — patterns from real deployments.
P.52The AI industry is pivoting from massive models to efficient SLMs offering 10-30x reductions in latency and cost. Learn why smaller is better and how to leverage SLMs in your applications.