The open AI ecosystem had a rough fortnight. NVIDIA agreed to acquire Hugging Face for $13 billion, which is the kind of headline that makes you do a double take at your coffee. Hugging Face is where most of us go to pull models, browse papers, and host inference endpoints. Jensen Huang now owns it. The community reaction on Hacker News ran nearly 2,000 points and ranged from “open source wins” to genuine concern about platform neutrality, and I don’t think either camp is entirely wrong. NVIDIA has a strong business incentive to keep HF useful to the ecosystem, but they also have a strong incentive to steer it toward their hardware. Worth watching closely.
The same fortnight, OpenAI announced it’s ending its contract with Cursor following Cursor’s acquisition by SpaceX. The cutoff is November 12, which gives developers some runway, but the message is clear: tooling you build a workflow around can become a platform decision overnight. If you’re heavily invested in Cursor, now is the time to check what your fallback looks like.
In this edition of AI++ we take a look at who’s quietly reshaping the tools we all depend on, get practical with RAG architectures and agent memory, and find out what Claude Fable 5.1 can do with a pelican.
Erick Ramirez | Apache Cassandra committer & Developer Advocate at IBM/DataStax
🛠️ Building with AI, Agents & MCP
Who controls your tools
The NVIDIA/Hugging Face acquisition and the OpenAI/Cursor split aren’t just business news. They’re a reminder that the open AI stack is less independent than it looks. Three or four companies control the model weights that matter, the chips those models run on, and increasingly the hosted tooling developers use to access both. If you’ve been treating any single provider as infrastructure, this is a good fortnight to stress-test that assumption.
On the more immediately useful end, the Warp team wrote up how they built a self-improving code review agent on Claude. Manually rewriting prompts on each observed failure didn’t scale, so they built a feedback loop that captures failure signals and feeds them back into the agent’s own context. The technique generalises well beyond code review. Anthropic also published an AI-native SDLC playbook with concrete patterns for running Claude Code in CI: eval harnesses, intent.md structures, and how to wire a stateless agent loop into a GitHub Actions pipeline.
Agents and memory
One of the most-discussed Hacker News posts this fortnight was Cal Paterson’s Memoryfields, a proposal for storing agent memory as portable Markdown files with optional YAML metadata and a SQLite vector index. The design principle is “more model, less mechanism”: instead of navigating a bespoke API maze, the agent reads and writes structured text it already understands. As models improve, the memory quality improves automatically without any changes to the mechanism, and the open RFC-style spec means your memory isn’t locked to a specific harness or provider.
A related failure mode worth designing against: on r/mcp, a developer reproduced a stale-evidence failure at an MCP tools/call boundary where an agent approved an $8,000 transfer based on a balance it had observed earlier in the session. The state had since changed. The tool call was structurally valid, but the reasoning behind it had expired. It’s the kind of bug that doesn’t show up in happy-path evals. Also worth noting: on the same subreddit, one developer measured that lazy-loading the MCP tool manifest past about ten tools cut context by 67% and meaningfully improved tool selection accuracy. If your agent has a lot of MCP servers behind it, that’s a cheap win.
RAG in practice
The Lighthouse Newsletter published RAG Is Simpler Than You Think, one of the cleaner decision trees I’ve seen for choosing between BM25, dense retrieval, hybrid, query rewriting, query decomposition, and full agentic retrieval. The framing I found most useful: query rewriting lets you fix retrieval quality by adjusting a system prompt instead of re-embedding an entire corpus. That’s a very different operational tradeoff. 515 Hacker News points and 216 comments suggests it landed for a lot of people.
On the cost side, one developer on r/RAG compared several approaches to cutting OpenAI embedding costs on a reindex-heavy pipeline and found that per-token pricing punishes nightly re-indexing hard. Change detection (skipping unchanged chunks), dimension reduction, and batching helped most. Another developer profiled LangChain’s overhead in their RAG query path and ended up moving it off the hot path entirely: they were only using it as a model-provider abstraction layer and the overhead wasn’t worth it. Worth checking if you’re in the same situation.
Coding agent hygiene
Fabien Sanglard published his agent.md file for improving LLM-assisted coding quality, and it’s immediately useful. The idea is simple: place a file in your project root with coding style rules and expectations, and the harness injects it at session start. Fabien’s version covers things like keeping context short by starting a new session per feature, and asking the agent to explicitly reload the file when output quality drops. You can also ask the agent to update it for you. 415 Hacker News points for “here’s my config file” is a strong signal that people were glad someone wrote this down.
🧠 New models
- GLM-5.3 is open-weight from Z.ai, benchmarking competitively against GPT-class models and already on Hugging Face. If you’re evaluating open-weight frontier options, this one is worth a run.
- Qwen3.8-Flash-Next — a new Qwen3.8 checkpoint with improved speed. The interesting story is the companion slotstream project, which lets you run the 104GB model on a 48GB Mac at roughly 12 tokens per second through memory streaming. Local inference on hardware that shouldn’t be able to fit it.
- Claude Fable 5.1 — Simon Willison’s quick note confirms a new Fable-tier drop. He used it to make an animated pelican, which is not a benchmark but is at least memorable.
- OpenAI Jalapeño — OpenAI’s custom inference chip, with first public benchmark numbers showing higher throughput and lower latency than current hardware. Useful context for anyone modelling hosted-inference costs over the next hardware cycle.
🗞️ Other news
- New MCP Roadmap from the Core Maintainers: five priority areas for the next spec release — agentic messaging primitives, HTTP-native transport unification, agent identity and enterprise security, improved primitives, and SDK developer experience. If you’re building on MCP, this is the document to bookmark.
- LangSmith LLM Gateway is in public beta — a governance layer between your agents and model APIs, covering spend caps, rate limits, model fallbacks, and PII redaction without provider lock-in. Available now for Plus and Enterprise plans.
- Anthropic published a report on automated alignment researchers: Claude running autonomous loops of literature search, method proposal, training, and testing to reduce deception, sycophancy, and jailbreaks in other models. They’ve open-sourced the harness. I’m genuinely uncertain whether “AI improving AI safety” is reassuring or the premise of a film I’ve already seen, but the open-source release makes it worth watching.
- Domain-Driven Agents — a practical post on applying domain-driven design principles to agent prompts when integrating with legacy systems. Clear domain language cuts the context confusion that generic prompting accumulates over a messy codebase.
- EvoUndo is a paper on requiring self-modifying agents to prove recoverability before committing runtime mutations to their own prompts, tools, or middleware. If your agents modify themselves at runtime, the failure mode this addresses is real.
- OpenClaw 2.0 — 16,000+ PRs in a single release covering memory, skills, models, automations, and a rebuilt browser experience. The largest update to the self-hosted agentic desktop environment to date.
- Simon Willison notes that Codex now bundles LibreOffice. No further comment necessary.
🧑💻 Code & Libraries
- slotstream is a memory-streaming engine that enables running oversize quantised LLMs on consumer hardware by streaming model weights from disk during inference
- OneCLI is an open-source sandboxed agent harness designed for team deployment, with approval flows and isolation between agent runs
- diffium-db is a live TUI that shows you what an agent or migration is changing in your database in real time, with a before/after diff pane
- aimake is an incremental build system for AI and ML pipelines that only re-runs the stages downstream of what you changed, so a single chunker tweak doesn’t re-embed your entire corpus
- ViteMCP is a fork of FastMCP tracking the 2026-07-28 MCP specification, for developers who want to stay aligned with the updated protocol
- AI Engineer Notebooks is a collection of free, framework-free Colab notebooks covering RAG, agents, and evals
🔦 Langflow Spotlight
Langflow 1.11.0 shipped first-class multi-vector retrieval with the new lfx-nextplaid extension bundle. The bundle brings ColBERT-style late interaction into your flows with no custom glue code: instead of comparing a single query vector against a single document vector, it scores token matrices against each other using MaxSim, which catches relevant passages that single-vector cosine similarity misses. It also includes ColPali-style visual document retrieval, where text queries are matched against image patch embeddings from page scans. That means you can retrieve from charts, diagrams, and scanned PDFs without any OCR in the loop. PLAID indexing keeps it fast at scale by reducing the multi-vector storage and search overhead to something practical. If your RAG pipeline handles anything beyond clean plain text, take a look at the write-up.
🗓️ Events
The New York Metro Open Data User Group is hosting an in-person evening at Two Sigma in NYC on September 17. The agenda has two talks directly relevant to this newsletter’s crowd: one on vector search in Apache Cassandra and one on setting up a hybrid RAG pipeline with Cassandra. Jay Goldberg opens with remarks on how Two Sigma uses open source, and there’s networking from 7:20 onwards. Pizza and beer at 5:00, which is the correct order of priorities. If you’re in New York, register on Luma.
David Gillardi and Tejas Kumar from my Dev Relations team are back livestreaming their build of Walfly, a wearable AI app built with IBM Bob. Is it a hat? A watch? Something you clip to your dog? Tune in to find out. Subscribe on YouTube to get notified when they go live.