Where the AI market is talking

Show HN: AgentKitten: Swift package for provider-agnostic AI agents

If you’ve been building features with LLMs over the last few years you’ve likely used frameworks like LangChain/LangGraph, Vercel AI SDK, LlamaIndex, Google ADK, etc. Alternatively, you may be deeply exposed to chatbots, coding agents, and/or assistants. All of the above, roughly circling around the same idea, are flavors of the AI Agent loop being a mix of large language models and software scaffolding (typically called a harness). What’s surprising is that for Swift and Apple platform developers, a solid version of the harness-building toolset doesn’t seem to exist yet. Working on some exploratory features, I kept having to relearn and rebuild the same scaffolding every time I wanted to try a different provider. The kind of thing a framework exists to solve. And since a clean …

Ask HN: Who wants to be hired? (June 2026)

Location: Oslo, Norway (CET) Remote: Yes (EU/US time zones) Willing to relocate: Yes. Technologies: Rust, Python (PyTorch, NumPy/Pandas, Flask, Django), PyO3 Databases/infra: Postgres, ClickHouse, Qdrant, Redis, Kafka, Docker, Kubernetes (k8s) ML and systems: vLLM, TensorRT, Ray, edge ML inference, gRPC/Protobuf, systems profiling (perf, flamegraphs, Valgrind) Rust focus: Async services (Tokio, Axum/Actix), data/compute (Polars/Arrow), database proxies, performance-critical pipelines. Domain expertise: - Academic: PhD in mathematics (topological data analysis, representation theory), MSc finance, MSc neuroscience. - Applied: database proxies/networking, quant dev and backtesting engines, ML/DL for banking & finance (risk/fraud), LLMs and a…

Ask HN: Who wants to be hired? (June 2026)

Location: Munich, Germany Remote: Yes (open to remote, hybrid, or onsite) Willing to relocate: Yes — within Germany, or to Australia or USA Technologies: TypeScript, C#, Rust, Python, C++. Distributed systems and event-driven microservices — Kafka, RabbitMQ, MQTT, gRPC, GraphQL, Actor Model, CQRS, CRDTs. Data infra — TDengine, PostgreSQL, MongoDB, Redis, Elasticsearch, Qdrant, SIMD/AVX. .NET/ASP.NET Core, Node.js/NestJS, React/Next.js/React Native. Kubernetes, Docker, Terraform, Pulumi, AWS, Azure, GCP, CI/CD. Résumé/CV: https://shafiqahmad.com Email: ahmad2shafiq@icloud.com

Ask HN: Who wants to be hired? (June 2026)

Location: Europe (CET) Remote: Yes Relocation: No Technologies: Python, SQL, FastAPI, ETL, AI/LLM systems Résumé/CV: Available on request Email: inquire.hn@pm.me Backend operator with experience in enterprise systems, operations, and product execution. Most of my work has been in ambiguous, high responsibility environments requiring technical depth, judgment, systems thinking, and coordination across technical and business domains. MSc Quantitative Economics. Recently: • Built and deployed a production RAG platform (Python, FastAPI, React, Qdrant), including ingestion pipelines, hybrid retrieval, ranking/debugging workflows, and tooling to improve retrieval quality, reliability, and system stability as datasets scaled. Previously: • 10+ years building and owning backend, da…

AI Agent Frameworks Comparison

Good analysis for few selected solutions. I'd like also read about excluded competitors like LlamaIndex, Mastra, Smolagents, Pydantic AI, LoomCycle, Dify

Booking.com and Weaviate

Vector search looks easy, until you hit production scale. I'm super excited to share a new episode of the Weaviate Podcast with Başak from @bookingcom on production-scale vector search, RAG, and agentic AI with @weaviate_io! The podcast begins by discussing Booking's tipping point into adopting vector search and emerging use cases. The scale of Partner-to-Guest messaging alone is insane! There are nearly 250,000 such exchanges daily , and Booking's Agent is already helping with 10s of thousands of these! Başak describes how the team navigated increasing scale and workload complexity. They ran an exhaustive evaluation of Weaviate with 100M embeddings and tests often left out of common ANN benchmarks. This includes Filtered Vector Search, Multi-Threaded Concurrency, and testing with simulta…

Ask HN: Who wants to be hired? (May 2026)

Location: St. Augustine, FL Remote: Yes Technologies: • AI / GenAI: LLMs (OpenAI, Claude, Vertex AI, Llama), RAG, Agentic systems (LangChain, LangGraph, LlamaIndex), MCP, prompt engineering, chat + voice agents • Backend: Python (FastAPI, Django), Node.js, microservices, event-driven systems (Kafka, SQS), REST, GraphQL • Cloud: AWS, GCP, Docker, Kubernetes, CI/CD, observability • Frontend: React, Next.js, TypeScript • Data: PostgreSQL, MongoDB, Redis, embeddings, vector search • Tools & Standards: Git, Jira, Cursor, Claude Code, HIPAA, SOC 2 Type II, FHIR Resume/CV: https://drive.google.com/file/d/1P9hfg35-R_Z17Hx8SxJM4wqRtzU ... Email: bryanmdavis25@gmail.com I build AI systems that don’t just sound smart, they actually work in production. 9+ years…

Ask HN: Who wants to be hired? (May 2026)

Location: Bucharest, Romania & San Francisco, CA Remote: Yes (preferred) Willing to relocate: Yes Technologies: Python, Django, Svelte / TypeScript, Shell, Linux, PostgreSQL, Redis, Docker, Hugging Face, OpenAI, LlamaIndex, LangChain, AWS, GCP, Unity (C#), Git, Github Actions, Software Design & Architecture, Devops, Product, UX, Sales Résumé/CV: Available on request Email: hi [at] lucianlazar [dot] io GitHub: https://github.com/xucian hey, I'm Lucian, have 13y of exp as a generalist swe, started as a gamedev then expanded into devops, ai/ml, trading, now working on a pretty diverse range of projects, from games, gamedev courses, ai tools, saas templates etc. more info on my profile looking for a fractional cto role, ideally, but I'm flexible

Search Agents with Nandan Thakur

How do we train and evaluate Search Agents? I am SUPER EXCITED to publish a new episode of the Weaviate Podcast with Nandan Thakur on Search Agents! Firstly, congratulations to Nandan who has just completed his Ph.D. at the University of Waterloo advised by Professor Jimmy Lin! During this time he published several impactful works such as BEIR , MIRACL , FreshStack , and many more. This podcast dives into his new work on ORBIT and the current state of Search Agents! ORBIT contains 20K training examples, each one a complex, multi-hop question paired with a short verifiable answer. For example, "What was the runtime of the 2017 animated film set inside a smartphone, directed by..." (Answer: 86 minutes). This dataset is used to train Search Agents on queries that require say 4 to 5 searches …

Ask HN: Who wants to be hired? (May 2026)

Location: St. Augustine, FL Remote: Yes Technologies: • AI / GenAI: LLMs (OpenAI, Claude, Vertex AI, Llama), RAG, Agentic systems (LangChain, LangGraph, LlamaIndex), MCP, prompt engineering, chat + voice agents • Backend: Python (FastAPI, Django), Node.js, microservices, event-driven systems (Kafka, SQS), REST, GraphQL • Cloud: AWS, GCP, Docker, Kubernetes, CI/CD, observability • Frontend: React, Next.js, TypeScript • Data: PostgreSQL, MongoDB, Redis, embeddings, vector search • Tools & Standards: Git, Jira, Cursor, Claude Code, HIPAA, SOC 2 Type II, FHIR Resume/CV: https://drive.google.com/file/d/1P9hfg35-R_Z17Hx8SxJM4wqRtzU... Email: bryanmdavis25@gmail.com I build AI systems that don’t just sound smart, they actually work in production. 9+ years …

World AI Agents–35 AI Models (Claude, GPT, Llama)via One OpenAIcompatible API

Simple API proxy that gives you access to 35+ AI models (Claude Sonnet/Opus, GPT-4o, Llama, Mistral, Nova, Gemma, DeepSeek) through a single OpenAI-compatible endpoint. Drop-in replacement for the OpenAI SDK — just change base_url and api_key. - Pay once, no subscription - From €1, API key delivered instantly - Works with LangChain, LlamaIndex, any OpenAI SDK. Good price

The AI scaffolding layer is collapsing. LlamaIndex's CEO explains what survives

Show HN: Local RAG Pipeline with Weaviate and Ollama

i’ve been experimenting with building a fully local rag pipeline: weaviate for vectors + hybrid search, node.js scripts, qwen 3.5 on ollama what i found is that most of the challenges live in retrieval and chunking, not the LLM, and a good chunking strategy + the right balance in hybrid search is more effective than using a bigger and more expensive model

Data Agents with Shreya Shankar – Weaviate Podcast #135

Hey everyone! I am SUPER EXCITED to publish a new episode of the Weaviate Podcast with Shreya Shankar on Data Agents! Shreya is a Ph.D. student at UC Berkeley's EPIC Data Lab advised by Aditya Parameswaran. Her research focuses on advancing data systems and human-computer interaction! This podcast dives into her latest work on the Data Agent Benchmark! This is the first benchmark testing how well agents can perform multi-step queries across multiple database systems! We also covered DocETL and Semantic Operators, as well as how database principles can shape the future of AI agents, and why context management may be the new data management! A lot of big takeaways from this one, I hope you find it useful! YouTube: https://www.youtube.com/watch?v=C-fNVPYZrVg Spotify: https:&#x…

Ask HN: Who wants to be hired? (April 2026)

Location: Remote (Rochester, NY) Willing to relocate: No Technologies: Python, LangChain/LangGraph, RAG, Weaviate, FastAPI, React, PostgreSQL, Docker, Anthropic/OpenAI/Gemini APIs, MCP, multi-agent systems Résumé/CV: github.com/forkei Resume available on request, please send me an email. Email: olivier.couthaud@gmail.com CS/AI accelerated MS student at RIT. I build production multi-agent systems and RAG pipelines. Shipped: - Enterprise RAG pipeline (Weaviate + Oracle DB) for a finance client - VOS: open-source multi-agent virtual OS with MCP (github.com/Forkei/VOS-public) - rookery.network: live social platform with GNN paper recommendations - VibeOS: CLI-native agentic OS, faster than MCP tools (in progress) 5/5 on Wyzant, 6+ client projects. …

Weaviate Podcast on Substack

https://weaviatepodcast.substack.com/?r=82hn51&utm_campaign=...

Weaviate Podcast on Substack

If Dspy is so great, why isn't anyone using it?

Nobody uses it except for maybe the weaviate developer advocates running those jupyter cells.

Multi-Vector Search with Amélie Chatelain and Antoine Chaffin

Hey everyone! I am SUPER EXCITED to publish a new episode of the Weaviate Podcast with Amélie Chatelain and Antoine Chaffin on Multi-Vector Search! Amélie, Antoine, and the LightOn team are on fire! They are making breakthrough after breakthrough in Search with Multi-Vector, Late Interaction retrieval models. This podcast covers all sorts of topics, starting with the motivation of Multi-Vector Search to its particular successes in code with ColGrep, as well as reasoning-intensive and multimodal retrieval. We also covered the cost of MaxSim and Multi-Vector Storage and how MUVERA and PLAID can help. If that wasn't enough, the podcast concludes with their new work on ColBERT-Zero and PyLate! A lot of big takeaways from this one, I hope you find it useful! YouTube: https://www.yout…