After Andrej Karpathy outlined the idea of “LLM Knowledge Bases” on April 3, 2026, developers built Graphify in under 48 hours. Graphify turns any folder of code, docs, and images into an interlinked markdown wiki you can query without vector databases or complex setup. It persists across sessions, builds backlinks and concept maps, and cuts future query costs.

This article shows how macOS 27 includes a built-in LLM accessible via /usr/bin/fm that runs entirely offline and needs no account. The author tests it on an M1 MacBook Air, noting its limited memory, occasional inaccuracies, and a range of Terminal-based tricks.

The article argues that speaking long, unfiltered “rambles” to an LLM captures your full train of thought and design trade-offs in a way short typed prompts can’t. It offers tips like embracing uncertainties, recording group debates, and voicing emotions to give the model enough context to make decisions that match your priorities. Finally, it shows how to set up a dictation app or transcription pipeline to feed those recordings into your coding agent.

The article compares the rise of large language models to the 1990s database boom, arguing that models may become ubiquitous and unnoticed—just like SQL engines today. It suggests the real story won’t be flashy launches but the “SQLite” of AI: the model everyone uses by default without ever choosing it.

This article argues that using Markdown prompts and LLMs as a runtime interpreter for business rules is slow, expensive, unreliable, and insecure. It shows how mixing regular code, human approvals, and LLMs without a solid framework leads teams to overuse LLMs instead of writing proper code and handling state continuations.

The article argues that large language models lack the structured data, integrity constraints, and deterministic processes that business software provides. Instead of replacing software, LLMs will plug into existing systems via protocols like MCP, serving as a flexible front end while databases and rule engines remain the backbone.

This article traces the path from RNNs to transformers, explaining why attention-based, non-recurrent architectures replaced older models. It then breaks down encoder vs decoder designs and shows how GPT’s decoder-only approach and tokenization power today’s large language models.

This issue rounds up dev tools and research, from a zero-latency domain autocomplete engine and Transformer internals to Go’s padding trick for faster clears. It also covers memcached vs Redis, using AI for large code diffs, building desktop apps with Deno, orchestrating agents with Orca, and GLM-5.2’s performance plus its head-to-head with Claude Opus.

Armin Ronacher breaks down two layers of agent loops: the internal cycle where a model calls tools and edits code, and the external harness that re-queues and re-runs tasks until a goal is met. He says loops shine for experiments, code ports, and security scans, but they generate brittle, over-defensive code and threaten human understanding, even as defenders must adopt loops to match automated attackers.

This unrolled thread covers four topics: a plain-language explanation of model weights, strategies for refining code with Perplexity AI’s Computer, an AI-native fund system built end-to-end, and tips for running LLMs locally on Apple Silicon using Ollama. It walks through each use case with examples and practical advice.

The author reruns security vulnerability triage experiments across 26 combinations of Claude and GPT-5 models with varying reasoning effort and context sizes. A four-model “council” achieved 86.2% unanimous votes, and GPT-5.4 at medium/high effort led overall performance, though full-chain solutions remained rare. The study also found higher reasoning sometimes backfires and function-level inputs outperformed whole-file analysis.

This article breaks down how Databricks’ ai_parse_document and ai_query functions simplify PDF extraction in a proof-of-concept but introduce hidden challenges—ongoing costs, duplicate processing, non-deterministic outputs, and input noise—when you scale to a reliable production pipeline. It walks through the core issues and why you need additional system design for checkpointing, deduplication, deterministic validation, and PII handling before using it on real healthcare data.

Simon Willison runs Claude Fable 5 through its paces, finding it slower and pricier than Opus 4.8 but far more knowledgeable thanks to its 1 million-token context. He tests it on real-world coding tasks—upgrading a MicroPython sandbox to full CPython in WASM and adding pause-resume hooks to Datasette Agent—showing it can build complex features end-to-end.

OpenAI bought Ona to power persistent, secure agents in its Codex platform, while Anthropic lifted its hidden safeguards after researchers flagged degraded outputs. The issue also covers Xiaomi’s MiMo Code AI assistant beating Claude on long tasks and dives into tokenizers, vintage LLM builds, compute markets, data debugging, and PyTorch optimizations.

This guide shows how to use Apple Silicon and the Gemma 3 270M model to fine-tune a small language model offline in under 10 minutes. It walks through installing the uv/MLX toolchain, preparing a simple code-review dataset, and running the training on any M-chip Mac without a cloud GPU.

The article shows that when an LLM evaluates if text meets a given criterion, the answer already sits in its hidden state before any token is generated. By capturing the hidden representation at a designated seed token and training a small MLP head (with optional LoRA sharpening and isotonic calibration), you get a fast, calibrated classifier that accepts arbitrary English criteria without per-criterion retraining.

Stanford posted a 1h44 CS229 lecture that explains how to build large language models from scratch. Engineers with those skills can command over $750,000 a year at firms like Anthropic.

This project packages four principles—Think Before Coding, Simplicity First, Surgical Changes, and Goal-Driven Execution—into a Claude Code plugin or CLAUDE.md file to curb LLM code pitfalls like overengineering and hidden assumptions. It enforces explicit reasoning, minimal edits, and test-driven success criteria to produce cleaner, more accurate AI-generated code.

This tweet notes that while CLAUDE.md solves the instruction-handling side, you still need to track your model’s context budget. It links to Headroom, a simple one-line-install status bar that shows your current context usage percentage in your editor’s status line.

PrismML’s Bonsai 8B trains a large language model with 1-bit weights from scratch, squeezing 8.2 billion parameters into just 1.15 GB. In benchmarks it ties or outperforms FP16 models like Llama 3.1 and runs at real-time speeds on phones, shifting the size-performance trade-off.

The article compares LLMs’ frozen knowledge to the amnesiac in Memento, showing how they rely on context prompts, retrieval systems, and external memory instead of updating their own weights. It reviews in-context learning and state-space memory layers, then argues that only continual learning—letting models compress new information into their parameters after deployment—can bridge the gap to genuine, scalable understanding.

The author connects a 16 GB Mac Mini to a 64 GB MacBook Pro using LM Studio Link’s encrypted mesh VPN, offloading heavy model inference to the more powerful machine without exposing ports or tweaking firewalls. This setup lets you run large LLMs on low-RAM devices as if they were local, with no cloud or API key hassles.

OpenAI trained a new LLM, GPT-Rosalind, on 50 common biological workflows and major public databases to help researchers navigate massive genomic and protein datasets. The model links genotype to phenotype, suggests biological pathways, and prioritizes potential drug targets by leveraging mechanistic understanding.

Yelp explains how it turned a two-week prototype into a scalable, production-ready AI assistant for business pages. They built near-real-time indices for reviews, photos, and structured data in an EAV schema, combined keyword-first retrieval with LLM prompts, and added query classification and trust-and-safety filters. The system streams answers with citations, logs metrics, and balances freshness, performance, and reliability.

This article breaks down the core concepts behind LLMs—from next-token prediction training to tokens, vectors and attention layers—to show how they generate text. It also covers context windows, parameters and why model scale affects performance.

This article reruns a 2023 benchmark with the latest LLMs, comparing direct SQL generation against querying through a structured dbt Semantic Layer. It finds that while text-to-SQL accuracy has jumped, a modeled Semantic Layer still delivers near-perfect, deterministic results for covered queries, making it ideal for complex or critical use cases.

Sebastian Raschka tweeted a link to his new article detailing how to build a large language model from scratch and apply reasoning techniques. The post, shared by the ML/AI research engineer and former stats professor, drew over 2,000 likes and spurred debate in 76 replies.

This article explores how advancements in software design, particularly through LLMs, shift the focus from using standard libraries to generating custom code. It highlights the implications for dependency management and emphasizes the need to understand the problem being solved rather than just the mechanics of coding. The author compares this shift to the evolution of 3D printing in manufacturing.

In a podcast discussion, predictions for the tech industry in 2026 are shared, highlighting the undeniable improvement of LLMs in writing code, advancements in coding agent security, and the potential obsolescence of manual coding. Other predictions include a successful breeding season for Kākāpō parrots and the implications of AI-assisted programming on software engineering careers.

The article analyzes the unit economics of large language models (LLMs), focusing on the compute costs associated with training and inference. It discusses how companies like OpenAI and Anthropic manage their financial projections and cash flow, emphasizing the need for revenue growth or reduced training costs to achieve profitability.