Shipping AI Agents in production is hard, here's what @cubic_dev_ learned:
Without @inngest:
❌ scattered observability
❌ unreliable LLMs
❌ Serverless timeouts
With @inngest:
✅ unified o11y
✅ flexible multi-agent orchestration
✅ reliable LLM calls and tools
Learn more 👇
⚓️ + 💳: The payment wall for browser agents has been shattered 🚀
Anchor is now the first browser agent to support Coinbase’s x402 payments protocol, bringing USDC-powered payments to the web of agents
👉 Anchor browser agents can pay for resources behind x402 enabled…
Everyone's trying to build "coworker" agents and while they can help you get shit done - they still...
> love to glaze you into rabbit holes
> don't connect with you emotionally
> feel like you're talking to a robot
Who's actually making agents emotionally intelligent?
We love Go (check our repo!)
@brunoscheufler, Senior Distributed Systems Engineer at Inngest, will talk at the GopherCon tomorrow in London 🇬🇧
Join us to learn more about reliable data migrations in Go ⚡
Is there a way to limit the amount of tabs that can be viewed at a given time? It feels overwhelming to see SO many tabs accumulate over time @ryolu_
Something like a Zentabs type of approach would be AWESOME 😎
Taking this a step further, I’ve started to see Claude + Gemini automatically create new tools to gather telemetry, test specific functions and interact with third party integrations / databases 💪
MCP is great, but watching Cursor build its own tools on the fly is magical 🪄
Taking this a step further, I’ve started to see Claude + Gemini automatically create new tools to gather telemetry, test specific functions and interact with third party integrations / databases 💪
MCP is great, but watching Cursor build its own tools on the fly is magical 🪄
Today, I encountered a build error and couldn’t determine which change caused it.
Since I create a separate Git branch for each feature, I followed this process using Claude code:
1. List all branches, sorted by date (newest first)
2. Run "pnpm build" on each branch, starting…
🧠 Use memory as tools with async processing for:
✔️ faster response
✔️ durable memory operations
@tedx_ai breaks down the patterns and the code using @mem0ai and AgentKit. Link below ⬇️
Inngest is now available on the @vercel marketplace:
🔌 Configure your Vercel projects to use Inngest without leaving your Vercel dashboard
⚡️ Preview deployments get automatically matched with Inngest environments
🧾 Unified billing and usage charts
Learn more ⤵️
We explore a new dimension in scaling reasoning models in Adaptive Parallel Reasoning
APR lets LMs learn to orchestrate both serial & parallel compute E2E via supervised training + RL — w/ better efficiency and scalability than long CoT on Countdown
🧵 arxiv.org/abs/2504.15466
Introducing: the Fetch APIs
The TypeScript SDK now offers the `step.fetch()` API and the `fetch()` utility, enabling you to request third-party APIs or fetch data durablely by offloading them to the Inngest Platform.
Learn more below 👇
Scaling intelligence is likely not just about building one omnipotent model (not in the short term at least) 🧠
The next-wave of intelligence will emerge as an agentic system of many models, long-term memory stores and real-time data fetching orchestrated through clever…
@Erwin_AI simplest answer possible:
build once, integrate everywhere
when you build an API, you need consuming applications to integrate all your endpoints
when building an MCP server on top of your API, applications require zero dev work to integrate your system (assuming those apps…
New Cursor update 0.49 is rolling out.
Cursor Rules:
1/ Generate rules directly with /Generate Cursor Rules
2/ Auto-attached rules apply smartly to matching paths.
3/ Always-attached rules now persist better.
Other:
4/ Chat history moved to command palette.
5/ Review changes…
🚀Introducing Scenario: a python agent testing library that uses an Agent to test your Agent 🤯
Scenario lets you define the situations your agent will be in for an automated AI tester to carry it over by simulating what real users would do automatically
GitHub link in the 🧵
With Llama 4's INSANE 10 million token context window, we will soon start to see...
🤖 Model distillation for agent optimization
🐝 24/7 swarms of cost-effective agents
🧠 Continuously learning/tuned agents
The question is, who will build the best open-source infrastructure to…
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