is this based on ARM? or x64
41 karma · joined September 3, 2021
so I wrote authsome. The bit I think is actually interesting is the run command:
authsome run -- python my_agent.py
the tokens are stored locally, encrypted, and refreshed before they expire. Oauth flows for interactive and headless, plus a browser bridge for API-key providers. There is a cli for pulling headers directly when you don't want the proxy.
the proxy only sees traffic that goes through it, so libraries that pin their own CA bundle slip past, also the streaming uploads and long-lived connections probably have edge cases I haven't hit. It's still alpha, v0.2.1.
Most interested in feedback on the proxy approach itself, that's the part I'm least sure about.
So built this, let me know what you guys think.
This covers: - ITAT (Income Tax Appellate Tribunal) - CESTAT (Customs, Excise & Service Tax Appellate Tribunal) - GST AAR (GST Authority for Advance Rulings)
- NCLT (National Company Law Tribunal) - IBBI (Insolvency & Bankruptcy Board of India) - DRT (Debt Recovery Tribunal) - SAT (Securities Appellate Tribunal) - CCI (Competition Commission of India)
- NGT (National Green Tribunal) - APTEL (Appellate Tribunal for Electricity)
- TDSAT (Telecom Disputes Settlement & Appellate Tribunal) - CAT (Central Administrative Tribunal) - AFT (Armed Forces Tribunal) - RERA (Real Estate Regulatory Authority)
Would love to pick your brains
The goal is to educate about MCP, answer questions, and cover use cases: RAG + MCP, IDEs + MCP, etc. We’ll have live demos, Pinecone folks talking about what they are up to, and much more fun!
If you have been early in the MCP race, this would surely be worth your time.
Why might this interest you?
Model Context Protocol (MCP) is a low-level JSON-RPC protocol for passing structured context and tools to an LLM. Instead of gluing prompts together, you expose one JSON endpoint for a tool (and it takes care of tons of API endpoints for that tool).
MCP is just REST for LLMs! It really is that simple!
We plan to show a live demo of a working MCP, preferably hosted one, setting up configs, with Claude.
We will also answer any questions!
Featured Speakers:
1. Michael Kistler - Principal Program Manager at Microsoft
2. Arjun Patel - Senior Developer Advocate at Pinecone
3. Santiago (https://www.linkedin.com/in/svpino/) - Computer scientist and teaches hard-core Machine Learning; will walk you through Why do we need MCP?, Before MCP vs. After MCP, Architecture, Primitives, and Advantages.
4. Alden Do Rosario - will dissect the RAG + MCP pipeline we run in prod, live demo.
Format: - 3×10 min tech talks (protocol, integration, case study) - 10 min panel on lessons learned - 20 min open Q&A - bring tough questions
When: - Date: Sept 25, 02 PM ET
Registration (free, no spam): LINK http://customgpt.ai/mcp-ama-hn
Code sample, and infra diagrams will be posted after the session. AMA during and after the call - hope to see HN folks there.
No vendor lock-in. No telemetry. No "premium" features.
We built this because we needed it. We're sharing it because you need it too.
What does it mean to you? 1. Free and Ready to use UI like ChatGPT with Voice 2. 100% Customizable for you to build on top of it 3. Dev community to fix bugs and feature requests 4. Why create RAG from scratch when you can use free templates?
Technical details: 1. Next.js 14 + TypeScript + Zustand for state 2. Proxy architecture keeps API keys server-side 3. Proper SSE streaming with cleanup and error boundaries 4. Voice: OpenAI Whisper STT + TTS (6 voices) 5. Three deployment modes: widget.js bundle, iframe, or standalone 6. PWA support with service worker 7. Dark mode + full mobile responsiveness.
Interesting challenges solved: 1. Concurrent message streams without memory leaks 2. Widget state isolation when multiple instances on the same page, 100% customizable. 3. CORS handling for cross-domain embedding 4. Citation preview just like ChatGPT
Deployment options: 1. Vercel/Netlify (one-click) 2. Railway/Render 3. Docker 4. Google Apps Script (single file, 20k req/day free for select social RAG AI bots)
Also includes 9 social platform bots (Slack, Discord, Telegram, etc.) that connect to the same CustomGPT.ai backend.
Code: github.com/Poll-The-People/customgpt-starter-kit
Demo: starterkit.customgpt.ai (10-min free trial or BYO key)
MIT licensed. No telemetry. No premium tiers.
We built this for ourselves but figured others might find it useful. Feedback welcome.
The goal is to educate about MCP, answer questions, and cover use cases: RAG + MCP, IDEs + MCP, etc. We’ll have live demos, Pinecone folks talking about what they are up to, and much more fun!
If you have been early in the MCP race, this would surely be worth your time.
Why might this interest you?
Model Context Protocol (MCP) is a low-level JSON-RPC protocol for passing structured context and tools to an LLM. Instead of gluing prompts together, you expose one JSON endpoint for a tool (and it takes care of tons of API endpoints for that tool).
MCP is just REST for LLMs! It really is that simple!
We plan to show a live demo of a working MCP, preferably hosted one, setting up configs, with Claude.
We will also answer any questions!
Featured Speakers: 1. Santiago (https://www.linkedin.com/in/svpino/) - Computer scientist and teaches hard-core Machine Learning; will walk you through Why do we need MCP?, Before MCP vs. After MCP, Architecture, Primitives, and Advantages.
2. Alden Do Rosario (CustomGPT.ai CEO) - will dissect the RAG + MCP pipeline we run in prod, live demo.
3. Roy Miara, (https://www.linkedin.com/in/roy-miara-73776a56/) Director of Machine Learning, Pinecone, will talk about what Pinecone is upto with MCP.
Format: - 3×10 min tech talks (protocol, integration, case study) - 10 min panel on lessons learned - 20 min open Q&A - bring tough questions
When: - Date: May 29, 01 PM ET | | May 30 At 1:30 AM IST | Thu May 29 At 8:00 PM UTC - Registration (free, no spam): LINK https://lu.ma/gr6eqznl
Code sample, and infra diagrams will be posted after the session. AMA during and after the call - hope to see HN folks there.
if I missed some tools, feel free to jot them below
We’ve just rolled out an OpenAI-Compatible Endpoint at CustomGPT.ai that should make it super easy to try Retrieval-Augmented Generation (RAG) in your existing OpenAI-based code.
Now, hundreds of tools in the OpenAI ecosystem can add RAG capabilities with minimal changes.
Docs here - https://docs.customgpt.ai/reference/customgptai-openai-sdk-c...
All you do is: 1. Swap your api_key to the CustomGPT one, 2. Change the base_url to our endpoint. And thats it.
You can keep using your OpenAI Python SDK code. Under the hood, we handle context retrieval from your project knowledge sources before generating a final answer.
We support the chat.completions endpoint with the same request/response structure. If you call an unsupported endpoint, we return a 404 or 501.
This opens up the entire ecosystem of OpenAI-compatible tools, frameworks, and services for your RAG workflows. Everything else—conversation format, message handling, etc.—remains the same.
Check out a quick Python snippet:
from openai import OpenAI client = OpenAI( api_key="CUSTOMGPT_API_KEY", base_url="https://app.customgpt.ai/api/v1/projects/{project_id}/" ) response = client.chat.completions.create( model="gpt-4", # We'll ignore the model param and use your project's default messages=[ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Who are you?"} ], ) print(response.choices[0].message.content)
We’re ignoring certain OpenAI parameters like model and temperature. If you try to call an endpoint we don’t support, you’ll get a 404 or 501. Otherwise, your code runs pretty much the same.
We built this because we kept hearing people say, “I’d like to try CustomGPT.ai for better context retrieval, but I already have so much code in the OpenAI ecosystem.” Hopefully this bridges the gap. Feedback and PR requests are welcome. Let us know how it goes!
Hope this helps folks who’ve been on the fence about trying RAG but don’t want to break everything they already have running!
If you have any question regrading the implementation, please ask below
1. Sync Postgres -> AWS S3 in parquet format [JSON dump and Level 1 flattened data support] 2. Sync Postgres -> Iceberg [in a few days, schema evolution support of Iceberg] 3. Sync Postgres -> Local Filestorage with everything we support for S3.
Want to test it out locally? Sync Postgres via OLake -> MinIO (using JDBC catalog) -> Query using Engines that support Iceberg V2 tables [doc launching pretty soon]
We talked with 100s of DE’s to understand their pain point and the two major issues for them were replicating Postgres and MySQL to a lakehouse format. They said, we delivered.
Our source connectors and writers are independent, meaning that after Apache Iceberg writers are being supported (PR - https://github.com/datazip-inc/olake/pull/113), any new connector will be able to dump to Iceberg with minimal changes!
Checkout GtiHub repository for OLake - https://github.com/datazip-inc/olake
Over time, many of us who’ve worked with data pipelines have dealt with the toil of building one-off ETL scripts, battling performance bottlenecks, or worrying about vendor lock-in.
With OLake, we wanted a clean, open-source solution that solves these problems in a straightforward, high-performing manner.
In this blog, I’m going to walk you through the architecture of OLake—how we capture data from MongoDB, push it into S3 in Apache Iceberg format or other data Lakehouse formats, and handle everything from schema evolution to high-volume parallel loads.
We are thinking to migrate from snowflake. Redshift and BigQuery seems good too but had to ask HN
Please have a look here, you will love it : https://github.com/zriyans/awesome-OS
Please have a look here, you will love it : https://github.com/zriyans/awesome-OS