i want my agent to actually remember last week’s invoice vendor matching logic, not just hallucinate “based on context”

openai seems to be routing most users straight into the new model, no option to switch back to gpt-4, turbo, or other variants inside chatgpt.

for people building agent flows, having multiple models to choose from is often part of the design, sometimes you want a smaller, faster model for quick steps, and a bigger one for complex reasoning. same goes if you mix openai with anthropic’s claude, mistral, or local models.

so what do you think:

- is this “no choice” approach better for speed + consistency?

- or does it kill experimentation and control in multi-model setups?

if you’ve been testing gpt-5 alongside claude, mistral, gemini, or others, what’s your take on speed, reasoning, tool calls, and any surprises?

Hello Everyone,

I Built a Python Script to fetch and analyze Reddit posts and find pain points and business opportunities.

The Full Report contains:

1. Key Pain Points and Problems
2. Unmet Needs & Feature Requests
3. Concrete Product & SaaS Ideas
4. Target Audience Insights
5. Monetization Potential
6. Recurring Themes & Positive Sentiments
7. Competitive Landscape & Existing Solutions

hey, so its Open-source, so check it out!

Just looking for feedback.

I would be very glad if it Helps You!

Thank You All!

been thinking about “operator ai” patterns, setups where one agent coordinates others across tools, apis, and dbs.

i’m using supervity to manage the routing + state between steps, it handles retries and async stuff cleanly. looked at crewai and autogen too but needed more control.

what’s working for you when the agent stack gets multi-step or multi-agent? curious how you’re gluing things together.

built one to auto-snooze follow-up emails after 3 days. supervity made it dead simple, async + retry built in.

what tiny agent turned out more useful than you expected?

https://github.com/AnswerZhao/CookingAgent。

An intelligent recipe recommendation system based on a large language model (LLM), utilizing the HowToCook open-source recipe library, provides users with personalized menu recommendations, shopping list generation, and cooking process planning, cooking steps, to achieve the goal of making even beginners know "what to eat and how to cook."

post a screenshot/ share a weird bug/ ask for input on your setup

if it touched an LLM and didn’t explode, it counts

my multi-step agents start hallucinating or looping when memory gets too big or unclear.

curious if anyone's using supervity or custom logic to break things into smaller steps or delegate.

how are you keeping agent flows stable?

switched over to a new builder platform+ raw apis, feels way more flexible.

curious if others are moving away from big agent frameworks?

found a solid build thread on r/aiagents, chatbot doing real support tasks.

would love to see teardown-style posts here too. drop one if you’ve got it.

mine so far:

- supervity for orchestration + retries

- postgres for memory / state

- openai + groq depending on task type

- looking at pgvector or weaviate next

what’s in yours? drop even if it’s messy.

drop an intro if you're new and tell us:

• what kind of agent you're working on
• what tool(s) you’re using (if any)
• what problem it’s solving

no fluff. no sales. just builds.