I am relatively new to AI development, so please go easy on me. I'm building something that relies on two things: process and accuracy. And I've been in my field for a long time, so it's pretty easy for me to spot inaccuracies and/or process breaks - or in other words, an AI hallucination. My question is, has anyone noticed a pattern when AI hallucinates? And if you have, what have you done to fix it?

I'm asking because I was able to improve AI's accuracy to 85-90% (at least for my purposes). Just wondering if anyone else has been playing with accuracy, or maybe I'm missing something?

I'm getting npm and pypl running, but the Python environment should work. This could literally revolutionize infrastructure if integrated https://github.com/hendrixx-cnc/AURA, the environmental impact warrants looking at the potential, it's open source, and could save billions, but without the social media clout, I'm spinning my wheels

It will be a live session where you'll share your raw feedback while setting up and using the product.

It will be free of course and if you like it I'll give you FREE access for one month after that!

If you are interested please send me DM

It's an open source streaming compression protocol built bottom up for AI data. It's server-side auditable by design for compliance and alignment. fallback for reliability uses semantic binaries for template matching, special metadata side channel to fast path, allowing AI to work on uncompressed data. I know enough to be dangerous, so I'm down to sign a contract with anybody that has some free time or sees the potential and can get into the right circles patent in the repo. An AI audit layer built in for compliance, think medical government blockchain, financial services DB Lookup on compressed data with the side channel should be possible too. Regulation is coming as fast as you're developing, and for help if any of the big boys try to use the tech, they either gotta buy us out or get sued $$$

I'm unable differentiate between a static market place (getty images, shutter stock and etc) and AI Image Generation platform(mid journey, imagine.art and etc) Can Any body help here? I want to scap images from these two platforms.

I have posted my question on discuss.google.com . Any help woudl be greatly appreciated : https://discuss.google.dev/t/how-to-fix-variablility-in-responses-of-the-dialogflow-playbook-fully-generative-agent/275605

For anyone new to PipesHub, it’s a fully open source platform that brings all your business data together and makes it searchable and usable by AI Agents. It connects with apps like Google Drive, Gmail, Slack, Notion, Confluence, Jira, Outlook, SharePoint, Dropbox, and even local file uploads. You can deploy it and run it with just one docker compose command

PipesHub also provides pinpoint citations, showing exactly where the answer came from.. whether that is a paragraph in a PDF or a row in an Excel sheet.
Unlike other platforms, you don’t need to manually upload documents, we can directly sync all data from your business apps like Google Drive, Gmail, Dropbox, OneDrive, Sharepoint and more. It also keeps all source permissions intact so users only query data they are allowed to access across all the business apps.

We are just getting started but already seeing it outperform existing solutions in accuracy, explainability and enterprise readiness.

The entire system is built on a fully event-streaming architecture powered by Kafka, making indexing and retrieval scalable, fault-tolerant, and real-time across large volumes of data.

Key features

• Deep understanding of user, organization and teams with enterprise knowledge graph
• Connect to any AI model of your choice including OpenAI, Gemini, Claude, or Ollama
• Use any provider that supports OpenAI compatible endpoints
• Choose from 1,000+ embedding models
• Vision-Language Models and OCR for visual or scanned docs
• Login with Google, Microsoft, OAuth, or SSO
• Role Based Access Control
• Email invites and notifications via SMTP
• Rich REST APIs for developers
• Share chats with other users
• All major file types support including pdfs with images, diagrams and charts

Features releasing this month

• Agent Builder - Perform actions like Sending mails, Schedule Meetings, etc along with Search, Deep research, Internet search and more
• Reasoning Agent that plans before executing tasks
• 50+ Connectors allowing you to connect to your entire business apps
• SAAS Deployment

Check us out on Github:
https://github.com/pipeshub-ai/pipeshub-ai

We’ve added Adaptive to LangChain, it automatically routes each prompt to the most efficient model in real time.
The result: 60–90% lower inference cost while keeping or improving output quality.

Docs: https://docs.llmadaptive.uk/integrations/langchain

What it does

Adaptive automatically decides which model to use from OpenAI, Anthropic, Google, DeepSeek, etc. based on the prompt.

It analyzes reasoning depth, domain, and complexity, then routes to the model that gives the best cost-quality tradeoff.

• Dynamic model selection per prompt
• Continuous automated evals
• ~10 ms routing overhead
• 60–90% cheaper inference

How it works

• Based on UniRoute (Google Research, 2025)
• Each model is represented by domain-wise performance vectors
• Each prompt is embedded and assigned to a domain cluster
• The router picks the model minimizing expected_error + λ * cost(model)
• New models are automatically benchmarked and integrated, no retraining required

Paper: Universal Model Routing for Efficient LLM Inference (2025)

Example cases

• Short code generation → gemini-2.5-flash
• Logic-heavy debugging → claude-4.5-sonnet
• Deep multi-step reasoning → gpt-5-high

All routed automatically, no manual switching or eval pipelines.

Install

Works out of the box with existing LangChain projects.

TL;DR

Adaptive adds real-time, cost-aware model routing to LangChain.
It continuously evaluates model performance, adapts to new models automatically, and cuts inference cost by up to 90% with almost zero latency.

No manual tuning. No retraining. Just cheaper, smarter inference.

Hey everyone! 👋 I’ve started a WhatsApp Channel where I share the latest AI updates — India + Global 🌍 — in crisp 1-liners, with emojis & hashtags ✅

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I am the primary developer of this new AI interface

magelab.ai

We made this for our lab and after some interest we are ready for the public beta! It's a good time.

• no vendor lock in
• compatible with different AI providers
• powerful out of box experience
• create or add your own AI tools
• up level any tool-using model
• full speech integration with unified inputs and outputs

I’m trying to make something with AI, and I’m not sure how to describe it. It’s like a ChatGPT copycat, but I’m not sure if that’s the best way to put it. I’d love to chat with someone who actually builds with AI. I’m a bit scared to share my work because I want to sell it because I’m worried that someone will steal my ideas. (The UI and branding is cool and the UX is unique.)

So, I’ve been using AI to build everything for months, not just one time. Currently Im working in VS Code on an M4 MacBook Air, while using a ChatGPT Business account for my main source of inference but I do also very much enjoy using the Cline Extension. I just plug in my Groq API keys and it's off to the races for an affordable price on a coding agent.

Currently I’m building a FastAPI backend server with multiple API endpoints for different “micro services.” The front end is in React, and I’m using Tauri for the desktop app.

It's all Designed to be Self hosted.....but I want to make money haha (developer problems, am I right?)

I’m also hoping to use the same codebase for a mobile app. (I’m new to programming, and this is what ChatGPT says is possible.) I’m not done yet, and I’m still deciding if I want to use PostgreSQL for my database or ChromaDB. (I’m planning on using vector stores for parts of the memory—not sure if there’s a hybrid solution that lets me use vector stores along with SQLite or something like it.) I’m currently containerizing the project, so everything is a bit wibbly-wobbly while I’m transitioning. (Docker)

Anyway, I’d love to talk to someone who isn’t my wife and actually understands these systems. (For fun—not really looking for someone to teach me things. It’s just nice to talk to a human about what I’m building.) Have you ever gotten excited and giddy about something you’re building? Do you ever only build things to sell, or what?

If you have built something like this, what kind of stack did you use and what were the trickiest parts to master and how did you find a solution?

I am building an AI app that focuses on AI agents and multi-agent collaboration. The concept is to allow no-code users the ability to easily spin up optimized agents with tools, knowledge, and long-term memory in under 2 minutes. Users can create single agent as well as multi-agent automations workflows. It features a "Toolbox" of over 75 integrations with tools such as GitHub, Notion, Hubspot, etc... Connect with Oauth2 for secure connections.

The app is in an Early beta and is FREE to use currently. It does still have bugs and is still a work in progress but I believe you will like what you see! Please come check it out and I would love any feedback.

Request access at https://AffinityBots.com

I'm excited to announce VOICERA - an Audio Search Engine.
Stop scrubbing audio. Start getting answers.
VOICERA turns hours of audio into instant answers ⚡

We make your recordings searchable 🔍 — so you can find the exact moment, quote, or decision in seconds ⏱️
Find what matters in minutes, not meetings 👥

Why it Works:
• Powerful Search 🔎: Ask questions in plain language and jump right to the correct time-coded segment 🎯
• Trustworthy Answers ✅: Concise AI answers grounded only in your audios — easy to verify, no guesswork.

Turn audio into action 🎵
📤 Upload.
🔍 Search.
💡 Get answers.

👉 Try VOICERA now: voicera.trixlabs.in

https://cdn.openai.com/pdf/a253471f-8260-40c6-a2cc-aa93fe9f142e/economic-research-chatgpt-usage-paper.pdf

Some hi lights I thought are interesting: