Attributed mentions
164
Across all collected Hacker News results
Discussion intelligence
A source-linked Hacker News monitor for the tracked company graph. These are attributable public discussions—not sentiment, endorsements, or unverified company facts.
Attributed mentions
164
Across all collected Hacker News results
Companies represented
12
Exact-name monitor matches only
Collection source
Hacker News
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Showing 121–140 of 164 matching discussions
Nothing is going to happen. Amazon is like a very large redwood tree that is all grown. It has no interest in fire, drought, animal life, human culture, or anything else you value. Your old Kindle is as one of its pinecones on a mantelpiece somewhere.
Location: Bhopal, India Remote: Yes, remote only Willing to relocate: No Technologies: Python, TypeScript, PyTorch, TensorFlow, Hugging Face, scikit-learn, RAG, LLM fine-tuning, LoRA, PEFT, LangChain, LangGraph, LlamaIndex, OpenAI API, Anthropic API, FAISS, Pinecone, Chroma, PostgreSQL, FastAPI, React, Docker, AWS, GCP, Azure Résumé/CV: https://adityasnehai.github.io/aditya-sneh-links/ Email: adityasneh09@gmail.com I’m Aditya Sneh, an AI/ML Engineer focused on applied ML, RAG systems, LLM applications, computer vision, and mobile-sensing AI. I have built production-style RAG systems, agentic AI tools, on-device ML systems, and real-world mental health/mobile sensing pipelines. I have also authored 5 AI/ML research papers. Looking for remote AI Engin…
Location: Bhopal, India Remote: Yes, remote only Willing to relocate: No Technologies: Python, TypeScript, PyTorch, TensorFlow, Hugging Face, scikit-learn, RAG, LLM fine-tuning, LoRA, PEFT, LangChain, LangGraph, LlamaIndex, OpenAI API, Anthropic API, FAISS, Pinecone, Chroma, PostgreSQL, FastAPI, React, Docker, AWS, GCP, Azure Résumé/CV: https://adityasnehai.github.io/aditya-sneh-links/ Email: adityasneh09@gmail.com I’m Aditya Sneh, an AI/ML Engineer focused on applied ML, RAG systems, LLM applications, computer vision, and mobile-sensing AI. I have built production-style RAG systems, agentic AI tools, on-device ML systems, and real-world mental health/mobile sensing pipelines. I have also authored 5 AI/ML research papers. Looking for remote AI Engin…
Location: Bhopal, India Remote: Yes, remote only Willing to relocate: No Technologies: Python, TypeScript, PyTorch, TensorFlow, Hugging Face, scikit-learn, RAG, LLM fine-tuning, LoRA, PEFT, LangChain, LangGraph, LlamaIndex, OpenAI API, Anthropic API, FAISS, Pinecone, Chroma, PostgreSQL, FastAPI, React, Docker, AWS, GCP, Azure Résumé/CV: https://adityasnehai.github.io/aditya-sneh-links/ Email: adityasneh09@gmail.com I’m Aditya Sneh, an AI/ML Engineer focused on applied ML, RAG systems, LLM applications, computer vision, and mobile-sensing AI. I have built production-style RAG systems, agentic AI tools, on-device ML systems, and real-world mental health/mobile sensing pipelines. I have also authored 5 AI/ML research papers. Looking for remote AI Engin…
Location: Washington, US Remote: Yes Willing to relocate: No Technologies: TypeScript, React, Next.js, Node.js, Python, FastAPI, Django, .NET/C#, PostgreSQL, Redis, Elasticsearch, GCP, AWS, Docker, Kubernetes, GraphQL, REST APIs, LangChain, RAG, AI agents, Vertex AI, Amazon Bedrock, Pinecone, Chroma, CI/CD, Prometheus, Grafana Résumé/CV: https://1drv.ms/b/c/72678D79CE44BE0F/IQB8hB_vXguzRJgoXbXd5se... Email: chadbooth93@outlook.com About me: I’m a Senior Software Engineer with strong full‑stack expertise in TypeScript, React, Next.js, Node.js, and Python, combined with hands‑on AI engineering experience building RAG pipelines, LLM features, and multi‑agent systems using OpenAI, Claude, Gemini, LangChain, Pinecone, and Vertex AI.
The last two years have been perfect for accumulating tech debt. 2023 you would have probably implemented your Agents with LangChain and RAG 2025 you'd use MCP and OpenAI/Anthropic Agent SDK. 2027 you will use a workspace frameworks (Amazon, Microsoft) sensor libraries and world models. Agents are a fantastic generational technologies, but in mid-2026 the environment they are operating in is quickly changing. The only way forward is to stay agile, understand model and vendor risk.
it's langchain, what did you expect
I think it's own sub-dir OpenWiki serves as an example of the sort of output you might get: https://github.com/langchain-ai/openwiki/blob/main/openwiki/... I agree with others this seems somewhat over-engineered; you can get similar results with a good prompt/skill; I guess the rest of the implementation here is intended as an agent-maintainer.
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
Location: Chicago, IL Remote: Yes Willing to relocate: No but open to relcoation occasionally like 1 or 2 times a year. Technologies: - AI Engineering & LLM Systems — LLMs (OpenAI GPT-4/5, Claude, Gemini, Llama, Mistral), Retrieval-Augmented Generation (RAG), AI Agents, Multi-Agent Systems, LangChain, LangGraph, LlamaIndex, MCP, Tool Calling, Prompt Engineering, AI Evaluation Frameworks, AI Safety & Guardrails, Conversational AI, Voice AI, Multimodal AI, Embedding Pipelines, Semantic Search - Software Engineering — Python, FastAPI, C#, ASP.NET Core, .NET Core, Node.js, TypeScript, JavaScript, React, Next.js, REST APIs, GraphQL, Microservices, Distributed Systems, Event-Driven Architecture, WebSockets - Cloud & Infrastructure — AWS, Azure, GCP, AWS Bedrock, Docker, Kubernetes, Terrafo…
Location: Rawalpindi, Pakistan Remote: Yes Willing to relocate: Yes if assisted. Technologies: LangGraph, RAG, FastAPI, Qdrant, AWS Bedrock, LLM agentic pipelines Résumé/CV: muhammadsamar.vercel.app Email: samarshehzad598@gmail.com
Thanks! You made me realize I completely forgot to mention memory in my post . For running memory, hex maintains a 10 message full-length context window, with the rest being summarized when the threshold is being crossed (15 messages) by a separate LLM call and stored in vector storage (Qdrant). Aside from that, observational memories get constantly stored in Qdrant as well for convenient retrieval and embeddings are calculated locally via Qdrant's FastEmbed. Preferences, facts and things like that are store in SQLite.
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…
Starbridge | Senior AI Engineer (Python) | NYC or Remote | Full-time | Starbridge is building an AI platform that turns large-scale public and enterprise data into reliable sales insights. We are early, moving fast, and building from zero to one, so this role will have huge ownership and product impact. AI Engineer: Applied AI plus strong software engineering. You will build, evaluate, and deploy LLM-driven features like deep document analysis and interactive chat, working with models from OpenAI, Anthropic, and Gemini. Expect hands-on Python, ML system design, experimentation, and production reliability. Bonus for RAG depth and frameworks like LangChain, LlamaIndex, or Hugging Face. We're looking to build our in-person team in NYC but also open to remote! Apply: https://starbri…
I found verified, live credentials leaking from Microsoft, Google, Red Hat, Grafana, and LlamaIndex in public GitHub repositories.
We built a LlamaIndex integration for SynapCores, an AI-native database that combines SQL, vector search, graph traversal, and AI functions in a single engine. The integration supports RAG, GraphRAG, hybrid retrieval, metadata filtering, and SQL-based querying via a single backend. We'd appreciate feedback on the API, developer experience, and any features you'd like to see.
Hi timvisee, Thanks for your work on Qdrant! I actually don't really have any complaints at this time. I'm using it in conjunction with Qwen3-VL to autocaption/describe images and then place them in a large vector Qdrant db so its easy to semantically search through ~100k worth of image assets.
Hi HN, I created AccuVideo because I couldn't find a privacy-respecting way to query and search inside my video library without uploading gigabytes of data to third-party clouds. AccuVideo runs on your local machine to index, transcribe, and let you chat with your video content. The indexed points are vectors are stored in Qdrant (cloud). We are launching today on Product Hunt and I'm offering free trials (generating licenses manually every hour). I would deeply appreciate the HN community's feedback on how to improve the local indexing pipeline. Also in product hunt today. Thanks for your time.
Methodology: HN Search returns recent public items matching a monitored company name. AIIStack stores a short normalized excerpt and the original link, deduplicates by company/provider/item ID, and creates an activity signal only after a threshold of newly observed records. Review the original discussion before making a decision.