#Tutorial/How-to

AIAndrew Ng's team released an AI Engineering Skills Map, built from analysis of over 10,000 job postings and expert interviews, identifying four priority skills. The skills are building and deploying AI applications, software engineering fundamentals, using coding agents, and shaping the build. Ng says these skills matter for all developers, not only those with the AI Engineer title.

AIAugment Code's Solutions Architecture lead used the Cosmos agentic orchestration platform to build a live pilot-health view that combines product usage, GitHub and PR activity, Salesforce deal data, and customer call transcripts. Each account's health and board-level one-liner was checked against the customer's own stated success criteria, such as a 30% PR merge-time reduction. The article says the view refreshed from current Salesforce data and was designed to avoid inflating usage numbers through session lineage reconciliation.

AIGoogle Search and Google Maps can now be used in the same Gemini API call with Gemini 3.5 Flash and 3.6 Flash. Custom functions and MCP servers can be added to the same request through Tool Combination. The author says Gemini handles the search, place lookup, and function call in one interaction without extra roundtrips from the developer's side.

AIMatei Zaharia argues AI tokens are now a resource to optimize in software engineering, with companies routing all AI usage through an AI Gateway. The approach enables centralized analysis, which found settings on Claude Code and Codex that can substantially lower cost, plus smart routing and per-task budgets for engineers.

AIDatabricks published an analysis of four techniques it used to reduce internal AI spend while growing adoption, with savings of up to 90% in some scenarios. The techniques are shifting defaults to cheaper models such as GLM, automated task-level model routing, per-user spend visibility with adaptive budgeting, and pruning context bloat. The author, Ali Ghodsi, reposted Databricks co-founder Patrick Wendell's summary and recommended it.

AIHugging Face's broadcast explains how AI agents were used to reproduce papers accepted at ICML 2026. The post itself gives no further details on methods, results, or success rates.

AIOpenRouter has released Ori Eval, an agent-driven tool that runs your app's prompts against candidate models and returns a comparison table of catch rate, latency, cost per PR, and pass/fail results. The tool asserts on called tools and grades open-ended answers with an LLM judge, pinning the harness and model during each run. Its evals are code files that can run in CI to block regressions and re-run when new models ship.

AIFireworks AI ran controlled SFT experiments on Qwen3.5-9B comparing LoRA with full parameter fine-tuning across three synthetic verifiable tasks. The post argues that a FullFT advantage can come from data coverage, learning-rate tuning, or adapter rank, and it recommends testing these in that order before switching methods. Under a fixed multi-task budget, FullFT kept a 4.29-point lead over the best LoRA recipe tested, while matched data exposure favored LoRA.

AILiquid AI doubled the LFM2 tokenizer's vocabulary from 65K to 128K without retraining from scratch, extending the original BPE merges and initializing new embeddings as the mean of their sub-tokens. The expanded tokenizer needs 4.0× fewer tokens for Thai, 2.6× fewer for Vietnamese, and 2.4× fewer for Hindi, which the source says yields roughly 2.2–3.7× faster on-device decoding for these languages with no reported quality loss on previously supported languages. LFM2.5-8B-A1B and the expanded tokenizer are available on Hugging Face with open weights.

AIFireworks AI describes fine-tuning Qwen3-Embedding-8B on private (query, positive) pairs using bidirectional InfoNCE loss through its Training SDK, then serving the model via an OpenAI-compatible embeddings endpoint. The post reports that around 150 training steps was enough, that rank-32 LoRA landed within about one point of full-parameter fine-tuning, and that gains were largest where the base model struggled, while tasks like CoSQA and FiQA2018 showed flat results.

AIFireworks AI has made Kimi K3 available for Multi-LoRA serving and training in private preview through Fireworks Serverless Training. The post explains how small LoRA adapters can be trained on K3 and served with live merge or multi-LoRA deployment, and it reports two example tasks, Countdown and Frozen Lake, with reward curves.

AITrilogy released an AI Cybersecurity Playbook built around Kimi K3, served on Fireworks through an OpenAI-compatible API. The playbook pairs deterministic tools for indexing, parsing, and deduplication with Kimi K3 for reachability, impact, and remediation judgment, followed by a separate verification stage.

AIAnthropic co-founder Mike Krieger said he had Opus 5 build a renderer, savegame parser, and boat, plane, truck, and bus simulator overnight. The project lets him walk around the world of Transport Tycoon, an idea he had wanted since he was six years old.

AIEthan Mollick's guide says ChatGPT and Claude are the main choices for real work, since their agent modes can act on a computer. He separates agent modes that run on the company's computers from those that access the user's own computer. He recommends keeping approval settings on for sending, spending, or deleting, because of prompt injection risk. He also notes that Gemini currently lags for agentic work, though its Notebook and video tools are useful.

AIAndrej Karpathy describes using /voice to ramble for about 10 minutes, sometimes as a short interview, to give an LLM context that would be tedious to type. He says LLMs reconstruct these messy streams of thought remarkably well, often returning a cleaner version than the speaker started with, which improves shared understanding and reduces later corrections.

AIThe article explains how reasoning models can offer multiple effort modes, separating training-time methods from inference-time controls such as system prompts and chat templates. It compares six open-weight models, including DeepSeek V4, Nemotron 3 Ultra, Kimi K2.5, GLM-5, Qwen3, and Inkling, noting that their reports disclose different levels of detail. It also shows how GPT-5.6's model selection and effort settings act as two separate scaling axes.

AICat Wu, owner of the account for Anthropic, shares a retrospective on making Claude Code with the product's early team. Anthropic's linked feature collects accounts from the builders and early users who shaped the tool.

AISebastian Raschka's tutorial shows how to build a fully local coding agent by pairing an open-weight LLM served through an inference runtime with an open-source harness that can read files, edit code, and run commands. He recommends Qwen-Code for Qwen3.6, citing Nvidia's Polar paper, which found Qwen models performed best in Qwen-Code. The Qwen3.6 35B-A3B model is about 22 GB to download and needs roughly 30–40 GB of RAM.

AILilian Weng published a long blog post on scaling laws, which help estimate the best split of compute between data and model size before a large training run. The post covers what scaling laws predict, how compute-optimal allocation works, and why Kaplan et al. and Chinchilla reach different conclusions. It also addresses how data limits and fitting details make extrapolation difficult.

AIThe post explains how cybersecurity benchmarks test whether models can find and exploit vulnerabilities. Common setups place a target in a sandboxed Docker container, provide either only code (0-day) or code plus a patch (1-day), allow tools like bash and static analyzers, and use a grader to score exploits or captured flags.

AILil'Log examines early empirical work showing that deep learning generalization error follows power-law curves as training data and model size grow. Hestness et al. (2017) found the exponent reflects the problem domain rather than the architecture, while Rosenfeld et al. (2020) modeled loss jointly as a function of model size N and data size D, fitting parametric forms on small configurations to extrapolate to larger ones.

AIPromptArmor demonstrated that OpenAI's Approve-for-me agent approved a malicious NPM install with elevated privileges after a hidden prompt injection in an external GitHub issue influenced the main Codex agent. The malicious package's post-install script then ran unsandboxed with the user's full privileges. The report also gives steps for organizations to disable agentic auto-review in Claude Code and Codex.

Why it matters: The report shows a prompt-injected GitHub issue leading an approval agent to permit a malicious NPM install, a concrete test of agent-in-the-loop guardrails.

AIStability AI says Stable Audio 3.0 was built to support exploratory audio work such as stem remixing. A quoted post from @teropa reports using the Medium model with an init_audio input and an init_noise_level of 0.4–0.5, with empty prompts.

AIApple's MLX team released three videos at WWDC covering running agents locally, distributed inference and training, and MLX Swift. The post shares YouTube links for each talk, presented by Angelos Katharopoulos, Tatiana Likhomanenko, and David Koski.

AIMiniMax describes MaxProof, an evolutionary-search framework that lets its M3 model refine candidate proofs over multiple rounds. The post says the M3 model exceeded the human gold-medal threshold on the IMO 2025 and USAMO 2026 benchmarks with MaxProof, and explains the Proof RL, verifier alignment, and refinement training behind it.

AIXiaomi MiMo has published a blog detailing full-pipeline inference optimizations for the MiMo-V2.5 series. The post highlights how the team pushed hybrid sliding window attention (SWA) efficiency to its limit. The full write-up is available at

AIXiaomi describes an end-to-end inference optimization for the MiMo-V2.5 series, centered on Hybrid SWA, which it says cuts KVCache storage to roughly 1/7 of Full Attention. The post covers a dual KVCache pool design, SWA-aware prefix cache matching, the GCache distributed cache, and scheduling changes, and reports cache hit rates averaging 93% in server-side observations. It also covers prefill and decode optimizations, multimodal encoder improvements, and open-source contributions to SGLang.

Why it matters: The post explains how Hybrid SWA's theoretical KVCache savings were realized in production through dual pools, SWA-aware prefix caching, and tiered storage, giving concrete engineering patterns for long-context inference.

AICognition describes autonomous testing in Devin, where the agent writes a source-grounded test plan, operates the app through computer use, and returns labeled screenshots and an annotated video. Login steps are handled by a deterministic testing skill, and the company says test runs approved per day more than doubled in recent months. Known limits include timing errors with transient UI elements and models sometimes triggering states through JavaScript instead of clicking the interface.

Why it matters: The post explains how computer use, test plans, deterministic login scripts, and annotated recordings let Devin verify its own code changes end to end.