Data1984

SLayer - an open-source semantic layer for AI agents. The usual argument for giving agents more context is that it helps them write better SQL. SLayer takes a different angle: the problem isn't that agents write bad SQL, they're generally fine at that, it's that they don't know which SQL answers the question, so they guess, and the guess looks just as confident as a correct one. Same question, different query, sometimes a different number. Instead of feeding the agent more context and hoping it reasons its way to the right query, SLayer gives it a governed set of pre-defined metrics to call. The agent still picks what it needs, but it never decides what "revenue" means or which tables to join, so the same question always returns the same, traceable answer. Built by the team behind Motley, running as a library, CLI, or MCP/REST interface, no separate service required. GitHub: github.com/MotleyAI/slayer X: x.com/usemotley Website: motley.ai/slayer

https://github.com/chopratejas/headroom

https://blog.2minutestreaming.com/p/what-is-a-lakestream

A drop-in Apache Spark replacement written in Rust, unifying batch processing, stream processing, and compute-intensive Al workloads. https://github.com/lakehq/sail

Karpathy posted his raw/ folder workflow for LLM context — a simple idea: dump your notes, PDFs, papers into one folder and feed it to the LLM. Someone built a full system on top of it for Claude Code. claude-memory-compiler hooks into your Claude Code sessions and automatically compiles them into a structured knowledge base. Every decision, pattern, lesson — extracted and cross-referenced into markdown articles. The architecture is interesting: • No RAG, no vector DB — just a smart index.md the LLM navigates • Works at personal scale (50–500 articles) better than cosine similarity • Runs on your existing Claude subscription, no extra API costs https://github.com/coleam00/claude-memory-compiler

I just learned you can run Claude Code locally with Ollama. Ollama 0.19 (preview, released yesterday) is now powered by Apple's MLX framework — and one thing that caught my attention as a Claude Code user: Ollama now reuses its cache across conversations, meaning less memory overhead and more cache hits when using a shared system prompt with tools like Claude Code. That's a meaningful improvement for agentic workflows. The setup is a single command: ollama launch claude --model qwen3.5:35b-a3b-coding-nvfp4 Whether you're working in an air-gapped environment or just tired of API costs — local coding agents are getting genuinely viable. (Requires a Mac with 32GB+ unified memory) https://ollama.com/blog/mlx

https://blog.jetbrains.com/pycharm/2026/02/langchain-tutorial-2026/

ClickHouse MergeTree and HBase are the same thing at their core. Not literally — but architecturally, they share the same DNA: the LSM Tree (Log-Structured Merge Tree). Here's how it works: 1. Writes hit memory first → fast, no disk I/O 2. When memory fills, flush to an immutable sorted file on disk 3. Background compaction merges files → removes duplicates, applies deletes 4. Bloom filters + sparse indexes make reads fast without scanning everything HBase calls these HFiles. ClickHouse calls them Parts. Cassandra calls them SSTables. Same idea. What ClickHouse adds on top: ★ Columnar layout inside each part (OLAP-optimized) ★ The merge step does useful analytical work — deduplication (ReplacingMergeTree), summation (SummingMergeTree), pre-aggregation (AggregatingMergeTree) ★ Sparse indexing at granule level (8192 rows) rather than row-level I still teach HBase in my data engineering course — as a NoSQL example and as a core part of the Hadoop ecosystem. And honestly, I started my DE career working with it. Sometimes I wondered: is this too specific? Should I simplify the curriculum and drop it? But my teaching philosophy has always been to explain technologies by focusing on what's fundamental and shared across many systems. And this connection — HBase and ClickHouse both rooted in LSM Trees — is exactly why that approach pays off. The tools change. The patterns underneath them don't.

🦐 PicoClaw is an ultra-lightweight personal AI Assistant inspired by nanobot, refactored from the ground up in Go through a self-bootstrapping process, where the AI agent itself drove the entire architectural migration and code optimization. ⚡️ Runs on $10 hardware with <10MB RAM: That's 99% less memory than OpenClaw and 98% cheaper than a Mac mini! https://github.com/sipeed/picoclaw

https://blog.dataexpert.io/p/the-2026-ai-data-engineer-roadmap

And here is the podcast with Boris, Head of Claude Code.

https://x.com/bcherny/status/2017742741636321619?s=20

Looks like a cool way for having a personal AI assistant, especially if you have an always on desktop machine at home https://github.com/moltbot/moltbot

Unlocking Entertainment Intelligence with Knowledge Graph | by Netflix Technology Blog | Nov, 2025 | Medium https://netflixtechblog.medium.com/unlocking-entertainment-intelligence-with-knowledge-graph-da4b22090141

An extensive guide on Snowflake query optimization. Also a talk by the authors if you prefer video.

https://github.com/karpathy/nanochat

LangChain raises $125M to build the platform for agent engineering https://blog.langchain.com/series-b/

dbt and Fivetran will merge. https://www.getdbt.com/blog/dbt-labs-and-fivetran-sign-definitive-agreement-to-merge