Attributed mentions
2835
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
2835
Across all collected Hacker News results
Companies represented
13
Exact-name monitor matches only
Collection source
Hacker News
Latest new record seen
Source-backed records
Showing 2761–2780 of 2835 matching discussions
I've been enjoying the Michael Caine voice clone narration of the Odyssey, but noticed it uses Greek names (Odysseus, Poseidon, Zeus), whereas the William Cullen Bryant translation ElevenLabs used as the basis for the audiobook uses the Roman names (Ulysses, Neptune, Jove). I tried reading the WCB translation while listening, but it got so confusing I had a go at making a companion edition that matches the names in the narration. It's free, non-commercial, and obviously not an official ElevenLabs product, just a fan attempt. Mostly for my own use, but thought I'd share in case it helps anyone else reading The Odyssey ahead of the Nolan film :)
I have developed an open-source memory system for agents accessible over MCP, which makes it possible to access it via any coding agent locally (claude-code) or via mobile (Claude AI, Mistral AI, etc). The primary storage mechanism is .md files stored as Notes in Nextcloud; however, since Nextcloud supports a rich ecosystem of apps such as documents, rss feeds, calendar, etc, your knowledge base can grow with you. All content can optionally be indexed and available via semantic search - powered by a Qdrant vectordb. The biggest cost drivers of a system like this is the memory required to host the vectordb - I'm really curious how others are optimizing their knowledge base. Thanks OP for doing to work in summarizing these tools! If you're interested either the MCP server or Nextcloud App f…
Full-Stack Developer | India (Open to Relocation & Visa Sponsorship) Tech Stack: React, Next.js, TypeScript, Node.js, FastAPI, PostgreSQL, MongoDB, Docker, GitHub Actions, REST APIs, AI/LLMs (LangChain, OpenAI, Gemini) Open-source contributor to Meshery (CNCF), KubeEdge (CNCF), and omegaUp. Built production-ready projects including Error404 UI and InsightCareer AI. GitHub: https://github.com/manishpatel00 � Portfolio: https://manishdevin.vercel.app Error404 UI: https://error404-ui.vercel.app Resume: https://drive.google.com/file/d/1g1feAoFWI7AVZBjEM0UYAFBpbr1... I'm interested in the Founding Full-Stack Software Engineer role and will also send my application via email. Thank you!
I addressed this a little bit in the comment below, but the cycles add up. I'm doing some pretty crazy things higher up in the stack, that I'm not quite ready to release yet. But even micro optimizations here add up at the scale I'm working at to allow me the headroom I need. I'm relying on a high-frequency recursive agentic loop that chokes a real-time guarantee without every optimization I can give it. I started by removing the IPC overhead from a weaviate db connection, and doing all my vector math in house with a lightweight sqlite db. This became the next obvious target for optimization once I saw how much that saved me doing things in-house.
I get what your saying, however we don't need to be stuck with the Claude code harness. You may find this interesting. https://www.langchain.com/blog/tuning-the-harness-not-the-mo...
Instrumentl | Senior (and above) Backend Engineer | REMOTE (US + Canada) | Full-time | 175,000 - 220,000 | Instrumentl is a profitable, hypergrowth, YC-backed SaaS platform building the operating system for grant-funded organizations. More than 5,500 nonprofits use Instrumentl to discover, track, and win grant funding, from local community organizations to the San Diego Zoo and the University of Alaska. Collectively they’ve moved over $1 billion through our platform. Looking for a strong Python engineer to build AI features end to end, from rapid prototype to production and the evaluation that keeps them honest. Join the engineering of 22 and company of almost 100 located all over the world. Note, AI experience is a plus, but not required. Stack: Python (FastAPI), Langchain, Postgres, Red…
Namecoach/Euphonia | Founding Voice AI Engineer (part-time, path to CAIO) | SF or Remote | REMOTE | Contract / Part-time, ~$30-120/hr by seniority, potentially converting to FT Namecoach has spent 10 years building the largest verified pronunciation dataset of its kind: ~2M self-recorded name pronunciations, plus a large corpus of human-verified phonetic spellings. We're now building Euphonia ( https://euphonia.namecoach.ai/ ), a provider-neutral middleware layer that makes any TTS engine pronounce names, acronyms, and other out-of-vocabulary terms correctly. The new wave of TTS (models from OpenAI, ElevenLabs, etc) sounds great but gives you almost no phoneme-level control, so it gets lots of names and words wrong and you can't easily fix it. We sit in front…
For my current purposes, I need a speech-to-text model/API to also emit word-level timestamps - for now, that makes ElevenLabs's Scribe v2 the best multiplatform, multi-language choice though it does look like this SpeechAnalyzer API provides them (although only for English).
Hi HN folks ! I am the author of AVA, a self hosted AI Voice Agent that plugs into Asterisk/Freepbx so you own all the aspects of an AI Voice agent in your own infrastructure. It uses Asterisk native Audiosocket/RTP with python engine to run STT,LLM and TTS loop. The project support several full providers openai, gemini, grok, elevenlabs out of the box and also provides options to build custom pipelines by choosing different stt tts and llm. It also supports full local agent if you have a GPU with 25GB RAM which enables realtime conversation along with tool calling. I started this as a hobby project last year when I started exploring voice agents and every saw every Saas tried to lock you in their eco system. Since then project has taken off and a lot of asterisk people started …
Location: Ahmedabad, India Remote: Yes Willing to relocate: No Technologies: Python, Django, DRF, FastAPI, PostgreSQL, LangChain, RAG pipelines, MCP servers, agentic AI workflows, Stripe integration Résumé/CV: https://www.linkedin.com/in/himanshu-more-dev/ ; https://todo-smoky-alpha.vercel.app/ Email: hmore7978@gmail.com Backend/AI engineer with production experience building RAG pipelines, custom MCP servers, and agentic workflows. Recently built an AI Todo Assistant (FastAPI + LangChain + MCP + Gemini Flash) and a multi-tenant SaaS app with real-time location tracking. Open to freelance/contract work, quick turnaround on RAG/agent integration tasks.
Sounds like a very cool project. I often use the ElevenLabs app/extension to listen to articles, but I would love to have a local option to do this. I'm on Windows though, so I look forward to your future updates.
Location: Skopje, North Macedonia Remote: Yes (CET timezone, comfortable with EU/US overlap) Willing to relocate: No Technologies: Python (FastAPI, LangChain, LangGraph), RAG systems (Neo4j GraphRAG, pgvector), LLM integration (OpenAI, Anthropic, local Llama via Ollama/vLLM), OCR pipelines (Google Vision, PaddleOCR), MLOps (MLflow, GitHub Actions CI/CD, Docker, AWS ECS/Fargate, ECR), classical ML (scikit-learn, regression and classification models), SQL/PostgreSQL, evaluation tooling (DeepEval, LangSmith), Frontend (React), Mobile apps (React-Native). Résumé/CV: https://github.com/hamzaarifi98/mycv Email: hamzaa.arifii@gmail.com Portfolio: https://github.com/hamzaarifi98
Location: Austin, TX Remote: Yes Willing to relocate: No Technologies: • Programming languages: Go, Python (FastAPI, Flask), Node.js, Java (Spring Boot), PHP. • Backend architecture: microservices, REST APIs, event-driven design, idempotency, concurrency. • Cloud and DevOps: Amazon Web Services (AWS) S3, Lambda, EC2; Kubernetes; Docker; CI/CD pipelines. • Data stores: PostgreSQL, MySQL, Redis, Memcached. • Messaging: Apache Kafka, RabbitMQ, Google Pub/Sub. • AI and machine learning: retrieval-augmented generation (RAG), vector databases (Pinecone, Weaviate), large language model (LLM) APIs. • Observability: Prometheus, Grafana, Datadog, Elasticsearch Logstash Kibana (ELK), distributed tracing. • APIs and security: gRPC, GraphQL, OAuth 2, JSON Web Tokens (JWT), role-based access …
Location: Austin, TX Remote: Yes Willing to relocate: No Technologies: • Programming languages: Go, Python (FastAPI, Flask), Node.js, Java (Spring Boot), PHP. • Backend architecture: microservices, REST APIs, event-driven design, idempotency, concurrency. • Cloud and DevOps: Amazon Web Services (AWS) S3, Lambda, EC2; Kubernetes; Docker; CI/CD pipelines. • Data stores: PostgreSQL, MySQL, Redis, Memcached. • Messaging: Apache Kafka, RabbitMQ, Google Pub/Sub. • AI and machine learning: retrieval-augmented generation (RAG), vector databases (Pinecone, Weaviate), large language model (LLM) APIs. • Observability: Prometheus, Grafana, Datadog, Elasticsearch Logstash Kibana (ELK), distributed tracing. • APIs and security: gRPC, GraphQL, OAuth 2, JSON Web Tokens (JWT), role-based access …
Data engineer with 3.5 years, built PySpark pipelines processing billions of records daily, dimensional models, and Unity Catalog governance serving 10+ teams. Recently hands-on with LLM systems: RAG on Bedrock, multi-agent POCs, MLflow tracing. Looking for data/AI engineering or forward-deployed roles,. Location: Austin, TX Remote: Yes (US) Willing to relocate: No Technologies: Python, SQL, PySpark, Databricks, Delta Lake, dbt, Airflow, AWS (S3, Glue, Bedrock, EMR), Snowflake, Redshift, Kafka, Docker, Terraform | AI: RAG, LangChain, CrewAI, MLflow, LLM orchestration Résumé/CV: github.com/analondhe Email: anaghaalondhe7@gmail.com
Location: Tashkent, Uzbekistan Remote: Yes Willing to relocate: Yes Technologies: Python, FastAPI, LangChain, RAG (pgvector, FAISS), classical ML (XGBoost, LightGBM, CatBoost, scikit-learn), React, Next.js, TypeScript, Flutter, PHP, MySQL/Postgres, Supabase, Docker Résumé/CV: https://drive.google.com/file/d/12O_duaC2O47aFvF_7OoSc97XEP9... GitHub: https://github.com/ioa2205 LinkedIn: https://www.linkedin.com/in/ibodullo Email: ioa22052005@gmail.com Full-stack AI engineer — production RAG systems, LLM integration, and full-stack apps/platforms built from scratch. Shipped an ERP voice AI agent across four production platforms, an AI-powered ATS (TezHR, live), and an EdTech platform (FastAPI/Supabase/Flutter&…
Rowboat markets itself as "local-first," but if you look in the actual codebase, transcription is Deepgram cloud, voice is ElevenLabs, analytics is PostHog, and the LLM is a cloud API.
Fair list: 1. True - no OS-level container today. The constraint is approval gating: consequential actions surface as a permission ask before they run (a separate supervisor LLM flags anything outside your intent). 2. True today. We'd deprioritized generic IMAP (drafts-on-thread is unreliable cross-provider), but you're the third person in this thread to raise it, so we'll scope it properly. 3/4/5. Today: Deepgram for STT, ElevenLabs for TTS, Exa for search. At least for search we supported more providers earlier (e.g. Brave) and found the assistant's skills degrade when they can't lean on provider-specific capabilities, like Exa's granular search. So we trimmed and went deep on a few. Feel free to raise a GitHub issue with what you want us to support and we'll do our best.
Location: New York, NY Remote: Yes Willing to relocate: Yes Technologies: Python, PHP, TypeScript, AI Agent, RAG, Prompt-Engineering, LangChain, LangGraph, React, Vue.js Résumé/CV: https://drive.google.com/file/d/1lycaftoYFy-Y-wmNJ70zUbliS3s... Email: kevintom9605@gmail.com Senior AI & Data Engineer with 8+ years of experience designing scalable data platforms, machine learning solutions, and Generative AI applications. Experienced in building enterprise data pipelines, cloud-native AI systems, Retrieval-Augmented Generation (RAG) platforms, agentic AI workflows, and production ML infrastructure on AWS. Strong background in Python, SQL, distributed data processing, vector databases, LLM orchestration, and MLOps. Passionate about delivering reliable AI solutio…
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.