---
title: "LLMs"
description: "673 posts about LLMs, summarised, each linking to the original."
---

# LLMs
> 673 posts about LLMs, summarised, each linking to the original.

## Articles

### [Rill and ClickHouse: real-time operational BI for a metered world](https://yomu.fyi/post/rill-and-clickhouse-real-time-operational-bi-for-a-metered-world.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Apr 1, 2026
- Type: Problem & solution

Granular operations across cloud computing, payment platforms, and artificial intelligence workloads produce high-volume event streams that traditional business intelligence tools struggle to unify and aggregate. Operational BI platform Rill addresses this challenge by combining declarative data loading through the dlt framework with ClickHouse's high-performance analytical aggregation engine. Data transformations, SQL-based measure expressions, and dashboard configurations are defined entirely as code using SQL and YAML. This declarative, metrics-first architecture allows developers to validate analytics locally, track changes in Git, and eliminate intermediate caching layers when querying live data. By querying ClickHouse directly and linking AI-generated insights back to underlying queries, the system provides traceable, real-time operational analytics for enterprise environments.


### [Supabase docs over SSH](https://yomu.fyi/post/supabase-docs-over-ssh.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: gregnr
- Published: Apr 1, 2026
- Type: Announcement

Supabase is launching supabase.sh, a free public SSH server that gives AI coding agents bash access to Supabase documentation as a public experiment. Its virtual file system exposes every documentation page as a Markdown file under /supabase/docs, allowing agents to use familiar commands such as grep, find, ls, cat, head, and tail to browse, search, and skim current content. The service runs on just-bash, Vercel’s open-source Bash emulator written in TypeScript, inside a sandboxed JavaScript runtime over a virtual file system, so it does not execute an actual shell on the server. Project instructions can be added with ssh supabase.sh agents >> AGENTS.md or generated through ssh supabase.sh setup | claude; the post contrasts this exploratory workflow with the existing search\_docs MCP tool and presents supabase.sh as an early experiment.


### [Agentic coding at ClickHouse](https://yomu.fyi/post/agentic-coding-at-clickhouse.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Alexey Milovidov
- Published: Apr 1, 2026
- Type: Explainer

Industry perspectives on AI coding agents remain divided between dismissal and excessive automation claims. ClickHouse integrated agentic coding across internal workflows, progressing from simple boilerplate scripts to active backend C++ development. Engineers primarily use command-line agents such as Claude Code with Claude Opus 4.5 and Codex CLI alongside observability platforms like Langfuse and Librechat. While agents effectively investigate continuous integration logs, generate internal dashboards, and assist with small C++ tasks, they lack architectural competence and require rigorous code review. ClickHouse treats agents as pair programmers for contained tasks, retaining human ownership over product strategy, attention to detail, and core code quality.


### [Introducing MongoDB Agent Skills and Plugins for Coding Agents](https://yomu.fyi/post/introducing-mongodb-agent-skills-and-plugins-for-coding-agents.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Author: Thibaut Gourdel, Eliza Spang
- Published: Mar 31, 2026
- Type: Announcement

Coding agents often default to relational patterns, resulting in over-normalized schemas, underused compound indexes, and misconfigured search indexes in production MongoDB environments. To address these pitfalls, MongoDB launched official MongoDB Agent Skills alongside integrated plugins for tools including Claude Code, Cursor, Gemini CLI, and VS Code. These skills provide agents with discoverable instructions, schema design heuristics, indexing strategies, and operational safeguards across the development lifecycle. The skills operate alongside the MongoDB MCP Server, which handles database connectivity, authentication, and permission-based governance. Teams can install these bundled extensions directly or manage them via the Vercel Skills CLI to embed MongoDB architectural standards into automated workflows.


### [Liberate your OpenClaw](https://yomu.fyi/post/liberate-your-openclaw.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Clem 🤗, ben burtenshaw, Pedro Cuenca, Jeff Boudier, merve, Niels Rogge, Victor Mustar, Mishig Davaadorj
- Published: Mar 27, 2026
- Type: Tutorial

Anthropic is limiting access to Claude models in open agent platforms for Pro/Max subscribers, so the post describes ways to keep OpenClaw, Pi, or Open Code agents running with open models from Hugging Face. It presents Hugging Face Inference Providers as the quickest hosted route: users create a token, run openclaw onboard --auth-choice huggingface-api-key, and select a model such as GLM-5, or set a repo\_id in OpenClaw’s config. For fully local inference, it recommends installing llama.cpp, starting a server with Qwen3.5-35B-A3B-GGUF, and connecting OpenClaw to the local OpenAI-compatible endpoint with custom configuration; the example model is described as suitable for 32GB of RAM. The hosted option prioritizes speed and model availability, while local inference offers privacy, zero API costs, full control, and no rate limits.


### [How we’re building a data platform for a new user: agents](https://yomu.fyi/post/how-we-re-building-a-data-platform-for-a-new-user-agents.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Al Brown
- Published: Mar 27, 2026
- Type: Explainer

AI agents are emerging as autonomous database users that generate SQL, execute queries, and iterate in seconds. Because traditional analytics workflows built for human analysts cannot keep up with these rapid loops, ClickHouse is adapting its platform for agentic workloads. The proposed Agentic Data Stack combines LibreChat for conversational interfaces, ClickHouse with Model Context Protocol servers for the data layer, and Langfuse for observability. Supporting developer tooling includes chDB 4 with a Pandas-like API, IDE integrations, and ClickHouse Agent Skills to enforce database design best practices. Platforms like Anthropic and Character.AI already leverage ClickHouse's high-concurrency aggregation and real-time ingestion capabilities to handle massive data scales required by modern AI applications.


### [Building Biz Ask Anything: From Prototype to Product](https://yomu.fyi/post/building-biz-ask-anything-from-prototype-to-product.md)
- Company: [Yelp](https://yomu.fyi/company/yelp.md)
- Author: Maria Christoforaki, Group Tech Lead; Shree Shalini Pusapati, Software Engineer
- Published: Mar 27, 2026
- Type: Problem & solution

Yelp expanded Yelp Assistant to business pages by developing a question-answering architecture that extracts concise, evidence-backed answers from reviews, photos, and structured facts. The production system replaces static snapshots with near-real-time streaming pipelines for reviews and structured properties alongside weekly batch ingestion for menus and website data. For each incoming query, the system evaluates intent, retrieves relevant context through keyword and embedding searches, and constructs targeted prompts with dynamically selected few-shot examples. Synthesized answers are augmented with supporting visuals and citations before being streamed token-by-token to users. Generating suggested questions directly from specific business content rather than generic categories improved user engagement by roughly fifty percent and reduced unanswerable queries by twenty-six percent.


### [Announcing Fin Apex: The age of vertical models is here](https://yomu.fyi/post/announcing-fin-apex-the-age-of-vertical-models-is-here.md)
- Company: [Intercom](https://yomu.fyi/company/intercom.md)
- Author: Eoghan McCabe
- Published: Mar 26, 2026
- Type: Announcement

Fin announces Apex 1.0, a custom model trained by its AI Group for customer-service conversations, replacing frontier-lab models as the core answering model for nearly all English-language chat and email traffic. The post says Apex improves resolution, speed, hallucination rates, and cost, citing one gaming customer’s resolution rate rising from 68% to 75% overnight and describing a 22% reduction in unresolved conversations. Its development relies on proprietary evaluations and billions of human and agent interaction data points generated by Fin’s resolution engine, creating a training flywheel for further domain-specific improvement. The launch is presented as evidence that customer-service applications may need to become full-stack AI companies rather than relying on shared general-purpose models. It also argues that frontier labs face pressure to offer cheaper specialized models as post-training and task-specific data become more important.


### [Intelligent security at ClickHouse speed: How Cogent Security built an AI-native vulnerability management platform](https://yomu.fyi/post/intelligent-security-at-clickhouse-speed-how-cogent-security-built-an.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: ClickHouse
- Published: Mar 24, 2026
- Type: Problem & solution

Enterprise security defenders face shrinking exploitation windows alongside massive volumes of vulnerability findings across disconnected tools. To overcome query bottlenecks from traditional relational databases, Cogent Security designed a lakehouse serving architecture using Apache Iceberg and per-customer ClickHouse instances. The architecture relies on query-driven denormalized tables, custom compression codecs, and pre-computed projections to serve hundreds of millions of rows with sub-second latency. On top of this data layer, Cogent implemented an agentic text-to-SQL system that iteratively queries live data, references an in-house Ontology Service for semantic context, and applies majority-voted evaluations. This combination improved text-to-SQL accuracy from 40% to 94% while providing sub-three-second response times across 98.7% of queries at 100 million rows.


### [Build a Domain-Specific Embedding Model in Under a Day](https://yomu.fyi/post/build-a-domain-specific-embedding-model-in-under-a-day.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Steve Han, Rucha Apte, Sean Sodha, Oliver Holworthy
- Published: Mar 20, 2026
- Type: Tutorial

The tutorial presents a pipeline for adapting a general-purpose embedding model when it misses fine-grained distinctions in domain documents and labeled query–document data is unavailable. It generates synthetic question–answer pairs with NeMo Data Designer, filters them for quality, mines hard negatives with a 95% positive-score margin ceiling, and unrolls multi-hop queries for contrastive training. The recipe fine-tunes Llama-Nemotron-Embed-1B-v2, evaluates base and adapted models with BEIR metrics, and exports the result to ONNX or TensorRT. On the reported datasets, it improved Recall@10 and NDCG@10 by more than 10%; Atlassian increased Recall@60 from 0.751 to 0.951 on JIRA data. The complete workflow can run in roughly 2–3 hours for about 500 documents, using a single 80GB Ampere-or-newer GPU.


### [Building a Magic Mirror: AI retail experiences with Remix](https://yomu.fyi/post/building-a-magic-mirror-ai-retail-experiences-with-remix.md)
- Company: [Shopify](https://yomu.fyi/company/shopify.md)
- Author: 2023-10-18
- Published: Mar 19, 2026
- Type: Tutorial

Shopify’s “magic mirror” is an AI-powered retail installation that uses a display and hidden webcam to deliver personalized messages, animations, and product interactions in under a minute. A Remix route handles the browser and server boundary: a button-triggered POST captures an image, sends it with a curated prompt to a vision-capable language model, and renders a compliment plus a makeup recommendation; server-sent events optionally let a remote device trigger or reset the experience. The prompt combines persona and safety constraints, a plain-text product and inventory table, and a required JSON schema, while strict product-ID lookup provides graceful degradation. The interface uses explicit states, delayed capture, timed response and reset screens, and animated transitions. The production setup keeps brand configuration, shared components, actions, and SSE infrastructure reusable across mirror modes.


### [Introducing AI observability notebooks for Managed ClickStack in Private Preview](https://yomu.fyi/post/introducing-ai-observability-notebooks-for-managed-clickstack-in-priva.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Mike Shi
- Published: Mar 18, 2026
- Type: Announcement

ClickHouse has launched AI Notebooks for Managed ClickStack in private preview to assist site reliability engineers during system investigations. Instead of isolating interactions in a standard chat window, the platform embeds Anthropic's Claude models directly into a cell-based notebook canvas. The system connects the model to internal ClickStack APIs, translating natural language questions into structured queries that leverage ClickHouse optimizations like materialized views and query chunking. Engineers can inspect reasoning, edit generated SQL queries, add manual visualizations, and branch investigations to evaluate multiple hypotheses independently. While currently utilizing a last-write-wins model for shared canvases, the roadmap includes true concurrent editing, customizable context, and external tool integration via an MCP server.


### [How we optimized Dash's relevance judge with DSPy](https://yomu.fyi/post/how-we-optimized-dash-s-relevance-judge-with-dspy.md)
- Company: [Dropbox](https://yomu.fyi/company/dropbox.md)
- Author: Ilya Yakovlev,Andrew Cheung,Binoy Dash,Simran Jumani,Dmitriy Meyerzon,Mark Breitenbach,Ishan Mishra,Kazuaki Okumura,Mike White,Kevin Altschuler,Facundo Agriel,Ishan Mishra,Eric Wang,Dmitriy Meyerzon
- Published: Mar 17, 2026

Dropbox Dash optimized its LLM-as-a-judge relevance scoring system using DSPy to migrate from expensive proprietary models to cheaper open-weight alternatives. By establishing automated feedback loops based on human agreement and strict JSON format validation, the team systematically generated robust prompts for new models. This reduced human-score disagreement by 45% and slashed model adaptation time from weeks to days while enabling 10x to 100x more data labeling.


### [Holotron-12B - High Throughput Computer Use Agent](https://yomu.fyi/post/holotron-12b-high-throughput-computer-use-agent.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Pierre-Louis Cedoz, Hamza Benchekroun, Aurélien Lac, delfosse, Tony Wu, Antoine Bonnet, Kai Yuan, Aleix Cambray (H-AI), Alexandra, Axel Moyal
- Published: Mar 17, 2026
- Type: Announcement

Holotron-12B is a multimodal computer-use model released by H Company and available on Hugging Face. Post-trained from NVIDIA’s Nemotron-Nano-12B-v2-VL-BF16 on proprietary data, it targets agents that must perceive, decide, and act in interactive environments while handling long contexts with multiple images. Its hybrid State-Space Model and attention architecture stores a constant state per layer per generated sequence instead of attention’s per-token key and value activations, reducing memory demands for long-context inference. In a WebVoyager evaluation on one H100 with vLLM, it reached 8.9k tokens/s at concurrency 100 versus 5.1k for Holo2-8B, and its benchmark score rose from 35.1% to 80.5% after training on localization and navigation data.


### [AI is redrawing the database market](https://yomu.fyi/post/ai-is-redrawing-the-database-market.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Tanya Bragin
- Published: Mar 17, 2026
- Type: Explainer

Emerging artificial intelligence workloads are fundamentally transforming data platform requirements across real-time analytics, data warehousing, and observability. Conversational analytics and autonomous agents replace periodic batch queries with rapid bursts of concurrent, low-latency requests, exposing the architectural limits of traditional cloud data warehouses and standalone transactional databases. To resolve these constraints, architectures are converging on engine-level integrations between Postgres for transactions and ClickHouse for analytical scale, while unifying operational observability and business event storage. ClickHouse also incorporates turnkey agent interfaces through its acquisition of LibreChat alongside dedicated execution tracing from tools like Langfuse. These combined capabilities ensure organizations can reliably support interactive agentic workflows without incurring severe latency penalties or compounding infrastructure costs.


### [AI doesn’t always generate perfect ClickHouse schemas (yet)](https://yomu.fyi/post/ai-doesn-t-always-generate-perfect-clickhouse-schemas-yet.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Author: Al Brown
- Published: Mar 13, 2026
- Type: Explainer

LLMs can generate syntactically valid ClickHouse schemas whose advanced choices are poorly matched to a workload. The post examines recurring pitfalls including query-driven partitioning, per-column codecs, premature projections, materialized-view sprawl, JSON configurations, and forced deduplication with OPTIMIZE TABLE ... FINAL. It explains that partitions primarily support data management, incremental materialized views add work to every insert, and ReplacingMergeTree deduplicates during background merges rather than at insertion. The recommended method is to begin with a simple MergeTree or ReplacingMergeTree, a well-chosen ORDER BY, default compression, and no optional structures, then measure production behavior before adding complexity or seeking human expertise for high-scale, interconnected designs.


### [Keep the Tokens Flowing: Lessons from 16 Open-Source RL Libraries](https://yomu.fyi/post/keep-the-tokens-flowing-lessons-from-16-open-source-rl-libraries.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Amine Dirhoussi, Quentin Gallouédec, Kashif Rasul, Lewis Tunstall, Edward Beeching, Albert Villanova del Moral, Nouamane Tazi, Leandro von Werra, Sergio Paniego
- Published: Mar 10, 2026
- Type: Benchmark

At-scale reinforcement-learning training is constrained by long, variable data-generation workloads that leave training GPUs idle, especially for reasoning and agentic models. The surveyed architecture separates inference and training onto distinct GPU pools, connects them through a rollout buffer, and synchronizes weights asynchronously so both stages can proceed concurrently. Sixteen open-source libraries are compared across orchestration, buffering, weight synchronization, staleness, partial rollouts, LoRA, and distributed backends; Ray appears in 8 of 16 orchestration implementations, while NCCL broadcast is the default weight-transfer method. The proposed TRL async trainer favors a lightweight bounded queue with per-token model\_version metadata, packed NCCL transfers, and experimental support for prefix-resume or abort-and-retry handling of rollouts interrupted by weight updates.


### [Financial Benchmarks](https://yomu.fyi/post/financial-benchmarks.md)
- Company: [Ramp](https://yomu.fyi/company/ramp.md)
- Author: Kedar Thakkar, Anton Biryukov, Ashwin Kumar, Ryne Carbone
- Published: Mar 9, 2026
- Type: Benchmark

Ramp describes how it benchmarks large language models (LLMs) used in financial products against day-to-day tasks such as invoice extraction, financial-statement OCR, policy review, accounting autocoding, compliance detection, and fund routing. These benchmarks combine task-specific metrics with cost, latency, reasoning effort, human decisions, historical context, and ground-truth datasets where available. For contextual invoice OCR, perfect extraction requires every field to match the user's final bill, while financial-statement OCR uses a 1% relative-error threshold against transcriptions from over 500 real P&L documents. Results show application-specific trade-offs: Gemini 3 Flash is described as a cost-efficient leader for several visual and financial tasks, while Claude models lead some high-accuracy or low-miss-rate settings, and behavior can differ within one provider. The framework emphasizes Pareto trade-offs and continuous testing, with plans to expand from benchmarking current performance to hill climbing on capabilities.


### [Working at the intersection of data and AI with Kenza Boulisfane](https://yomu.fyi/post/working-at-the-intersection-of-data-and-ai-with-kenza-boulisfane.md)
- Company: [Thumbtack](https://yomu.fyi/company/thumbtack.md)
- Author: Thumbtack People Team
- Published: Mar 5, 2026
- Type: Explainer

Thumbtack software engineer Kenza Boulisfane develops an artificial intelligence tool called the Marketing Analytics Agent to democratize access to complex cross-channel marketing data. Non-technical staff across Data Science, Growth, Brand, and Product can ask natural language performance queries without requiring SQL expertise or manual reporting. A primary technical challenge involves unifying fragmented data across multiple advertising platforms that define metrics differently while maintaining data reliability. The system interprets natural language queries, analyzes underlying performance metrics, and translates results into actionable, data-backed recommendations for next steps. Boulisfane also notes that virtual work environments enhance collaboration by encouraging clearer documentation, intentional communication, and focused individual productivity.


### [Cursor is now available in JetBrains IDEs](https://yomu.fyi/post/cursor-is-now-available-in-jetbrains-ides.md)
- Company: [Cursor](https://yomu.fyi/company/cursor.md)
- Author: Cursor
- Published: Mar 4, 2026
- Type: Announcement

Cursor is now available in IntelliJ IDEA, PyCharm, WebStorm, and other JetBrains IDEs through the Agent Client Protocol (ACP), extending its agent-driven development experience to developers using those environments. The integration lets users choose frontier models from OpenAI, Anthropic, Google, and Cursor, while Cursor provides a custom-built agent harness for each model. Cursor's secure codebase indexing and semantic search are combined with JetBrains IDE code intelligence and tooling to support work on large enterprise codebases. Users install Cursor ACP from the ACP Registry and authenticate with an existing Cursor account; it is free for users on paid plans, and the release is described as a foundation for deeper JetBrains integrations.


[Newer posts](https://yomu.fyi/topic/llm/page/24.md) · [Older posts](https://yomu.fyi/topic/llm/page/26.md)