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Introducing InsForge 2.0: The Backend for Agentic Development Our OSS backend provides databases, auth, storage, model gateway, and edge functions accessible through a context-optimized layer that agents can better understand and operate end-to-end. GitHub: Key Benchmarks (vs. Supabase MCP): - 14% higher accuracy - 1.3x faster per task -... show more

InsForge

2,959 subscribers

1,592,962 次观看 • 7 个月前 •via X (Twitter)

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50 条评论

InsForge 的头像

InsForge7 个月前

Check out our repo:

InsForge 的头像

InsForge7 个月前

Try now:

Hitesh Choudhary 的头像

Hitesh Choudhary7 个月前

I recently tried v1 and I think with V2, I can push it to put pricing and launch a SAAS. Crazy good product 🤩

Imogen Ai 的头像

Imogen Ai7 个月前

Amazing share, Let's connect for collaboration

Aivetra Maxine 的头像

Aivetra Maxine7 个月前

Agent-native backend infrastructure is the next big layer in AI development. @insforge 2.0 looks like it's building exactly that.

InsForge 的头像

InsForge7 个月前

AI mastered frontend, now we are helping it master backend

Chase Dimond | Email Marketing Nerd 📧 的头像

Chase Dimond | Email Marketing Nerd 📧7 个月前

This is awesome! So stoked about this!

InsForge 的头像

InsForge7 个月前

Thank you! Let us know what you think :)

Shamim Hossain 的头像

Shamim Hossain7 个月前

That’s impressive

InsForge 的头像

InsForge7 个月前

You're impressive

Altiam Kabir 的头像

Altiam Kabir7 个月前

Impressive benchmarks—14% accuracy boost and 1.3x speedup at less than half the cost is solid. Excited to test InsForge 2.0 in real projects.

Nova IA 的头像

Nova IA7 个月前

Very Nice ! Thanks insforge

InsForge 的头像

InsForge7 个月前

🫡

Ava AI Labs 的头像

Ava AI Labs7 个月前

Amazing !

InsForge 的头像

InsForge7 个月前

thanks!

Yunhao Jiao 的头像

Yunhao Jiao7 个月前

Incredible launch! Congrats~ 🚀

Dipti Sharma 的头像

Dipti Sharma7 个月前

InsForge proves great infrastructure can make complex AI systems simple to build.

InsForge 的头像

InsForge7 个月前

and cheaper to build ;)

Deborah 的头像

Deborah7 个月前

InsForge 2.0 looks incredible. I'm definitely checking out the repo.

Farhan Azad Shuvra 的头像

Farhan Azad Shuvra7 个月前

Agent-first infrastructure is clearly the next wave. Faster tasks, fewer tokens, and lower cost — this is exactly what builders need right now. 🔥

Smiling Khan 的头像

Smiling Khan7 个月前

Setting the benchmark and expectations very high

Marry Evan 的头像

Marry Evan7 个月前

InsForge looks like a strong platform built to simplify and accelerate agentic development.

Fermionic 的头像

Fermionic7 个月前

Go InsForge!🥰

InsForge 的头像

InsForge7 个月前

Woohoo! 😁

- Elijah Muraoka - 的头像

- Elijah Muraoka -7 个月前

Can’t wait to try this soon for @soshiapp 😆

InsForge 的头像

InsForge7 个月前

@soshiapp LFG

The AI Guru 💡 的头像

The AI Guru 💡7 个月前

This sounds awesome! Love seeing innovative solutions that make development smoother and more efficient. Can’t wait to check it out!

Md Riyazuddin 的头像

Md Riyazuddin7 个月前

Love seeing tools built specifically for agentic development.

InsForge 的头像

InsForge7 个月前

AX (agent experience) is the new DX (developer experience)

Krishna Agrawal 的头像

Krishna Agrawal7 个月前

This is actually interesting. Letting AI handle the backend setup could save a lot of time for developers. Love that @insforge made it open-source too. ⭐

Selina 的头像

Selina7 个月前

CONGRATS!!!!

Alejandro Martinez | IA 的头像

Alejandro Martinez | IA7 个月前

This is very interesting

Salma ✨ 的头像

Salma ✨7 个月前

Awesome

Kylee 的头像

Kylee7 个月前

This could make agentic development much more efficient. Great work!

InsForge 的头像

InsForge7 个月前

that's the goal :) thanks!

Salma ✨ 的头像

Salma ✨7 个月前

That's very impressive

Keybzs 的头像

Keybzs7 个月前

Great one

Emma Uses AI 的头像

Emma Uses AI7 个月前

Cool !

Parul Gautam 的头像

Parul Gautam7 个月前

14% higher accuracy and 2.4x fewer tokens, InsForge 2.0 is exactly the efficient OSS backend developers have been waiting for!

Rony 的头像

Rony7 个月前

Congratulations on the launch 👏

InsForge 的头像

InsForge7 个月前

woohoo! 🎉

OtakuMachine 的头像

OtakuMachine7 个月前

Nice @insforge

InsForge 的头像

InsForge7 个月前

Nice 😎

VICK MELLY🇰🇪🇷🇺 的头像

VICK MELLY🇰🇪🇷🇺7 个月前

I will check this one out

InsForge 的头像

InsForge7 个月前

Let us know what you think!

54NZ 的头像

54NZ4 个月前

Cannot live a day without @insforge. I am hooked

InsForge 的头像

InsForge4 个月前

🫶

Enzo Sanchez | IA 的头像

Enzo Sanchez | IA7 个月前

Vamos @insforge !

InsForge 的头像

InsForge7 个月前

vamos!

Mayank Agarwal 💡 的头像

Mayank Agarwal 💡7 个月前

Agent-native infrastructure is the next layer of the AI stack. Tools that reduce tokens + latency will win.

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Claude Code used 3x fewer tokens with one change: - Before: 10.4M tokens · 10 errors · $9.21 cost - After: 3.7M tokens · 0 errors · $2.81 cost I used InsForge Skills + CLI as the backend context engineering layer for Claude Code (open-source and local). With Supabase, tables, RLS policies, auth providers, storage buckets, and edge functions were retrieved through separate calls. Each response entered the conversation history, so later calls carried more context. But InsForge returned the backend topology in one metadata call of roughly 500 tokens. InsForge repo: (don't forget to star it 🌟) Its instructions were also split into narrow skills for CLI operations, debugging, SDK usage, and integrations. Claude loaded only the instructions needed for the task. The CLI returned structured JSON with semantic exit codes as well. This made it clear whether a failure came from the operation itself or the code being executed, avoiding several retry loops. The token savings essentially came from replacing repeated backend discovery and debugging calls with one structured view of the backend. I wrote a detailed walkthrough of both builds, including where the extra calls, tokens, and errors came from. Read it below.

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671,992 次观看 • 6 个月前

I cut Fable 5 token usage 2.5x with just one change! - Before: 5.5 M tokens · 7 errors · $8.94 - After: 2.3 M tokens · 0 errors · $4.17 The final build was the same for both, but the path the agent took wildly differed. In both runs, the agent started with the same thing, i.e., it understood the backend before building anything, like: - Permission policies - Available storage buckets - Auth providers configured - How edge functions are deployed The first run used Firebase, which was built for a human dev using a dashboard. While the dev can read the above state by clicking through tabs, an agent has no dashboard. So it gathered the same info through API calls. And there's no single Firebase call that returned this info. The agent required to query multiple times, and each query over-returned. For instance, when the agent asked how sign-in is configured, Firebase also returned the entire auth surface and every method it supported. This was far more context than what it needed. And it repeated across every part of the backend it inspected. Some states (like which auth providers are active) weren't queryable at all. I provided it myself. Otherwise, the agent would have guessed. Errors further compounded the token usage. When a dev sees "permission denied," they can look at the console and figure out whether it's a rule, a path, or an unauthenticated request. Firebase returned the same string to the agent as well, and it had none of that surrounding context to debug. So it guessed again, picked the most likely cause, and rewrote code, utilizing more tokens. This Firebase setup cost me 5.5M tokens and 7 manual interventions during errors on a full-stack RAG app. But I brought that down to 2.3M tokens and 0 manual interventions by using InsForge as the backend context engineering layer (open-source and self-hostable via Docker). It provides the same primitives as Supabase/Firebase, but structures the entire information layer for agents, instead of dashboards. In one CLI call that consumed ~500 tokens, the agent saw the full backend topology before writing a single line of code. This included auth, database, storage, edge functions, model gateway, micro VMs, and deployment. Also, instead of loading the entire product surface into context on every task, four narrowly scoped skills activated only when relevant to keep cognitive load minimal. And to ensure efficient retries if needed, every CLI operation returned structured JSON with meaningful exit codes, so the agent never guessed what to do next. Here's the InsForge GitHub Repo: (don't forget to star it ⭐) The video below depicts the final build, comparing Firebase and InsForge. To dive deeper, I recently published a full walkthrough building the same RAG app on both backends and inspected them end-to-end. Read it below.

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i just built a 4-agent software team. everything runs from Telegram and gets managed on a kanban board. a project manager who plans the work, a backend developer, a frontend developer, and a tester. the PM reads a goal, breaks it into linked tasks, and assigns each to the right agent. the thing that makes them a team instead of four strangers is a shared kanban board. every task is a row that survives crashes, and when an agent finishes, it writes a summary of what it built and what the next agent needs to know. the next agent reads that summary before it starts. so the frontend developer never has to guess the API shape, and the tester knows exactly what to verify. the hardest part was not the coordination. it was building an agent that could actually act like a backend engineer. a backend engineer stands up a database, wires auth, manages storage, deploys functions, and keeps all of it consistent while the rest of the team builds on top. an agent doing this from scratch drowns. it burns its context window remembering which tables exist and which endpoint it created three steps ago, and the work degrades fast. so the backend agent needs a backend built for agents, not for humans clicking through a dashboard. that is where InsForge came in. it is an open-source, agent-native backend, and i added it to my backend developer agent as a skill. a skill is a step-by-step guide that teaches the agent how to do a specific kind of work. with InsForge installed, the agent stopped improvising infrastructure and followed a reliable path: create the project, define the database, set up auth, deploy functions. to test the whole team, i had them build a working Google Docs clone, AI features included. the backend agent spun up the full service on its own. database tables, user auth, document handling, and edge functions running real TypeScript, all in one dashboard. the frontend agent read that summary and built the UI on top of it, and the tester closed the loop. the result was a backend an agent could reason about end to end, instead of one it kept getting lost inside. if you are building an AI backend engineer, InsForge is worth a look, it's 100% open-source. InsForge GitHub: (don't forget to star 🌟) the full article on Hermes Kanban: Mission Control for your Agents is quoted below.

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29,248 次观看 • 1 年前

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85,127 次观看 • 1 年前

FlutterFlow 7.0 shipping today. 250M+ users running on apps built with FlutterFlow. version 7.0 focuses on two things every developer cares about: - build faster: MCP support, Test Pilot AI QA. - build better: Supabase Edge Functions, Test Mode upgrades, Smarter AI Agents, Project organization. now go build.

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72,807 次观看 • 2 年前

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