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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 просмотров • 7 месяцев назад •via X (Twitter)

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Комментарии: 50

Фото профиля InsForge

InsForge7 месяцев назад

Check out our repo:

Фото профиля InsForge

InsForge7 месяцев назад

Try now:

Фото профиля Hitesh Choudhary

Hitesh Choudhary7 месяцев назад

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

Фото профиля Imogen Ai

Imogen Ai7 месяцев назад

Amazing share, Let's connect for collaboration

Фото профиля Aivetra Maxine

Aivetra Maxine7 месяцев назад

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

Фото профиля InsForge

InsForge7 месяцев назад

AI mastered frontend, now we are helping it master backend

Фото профиля Chase Dimond | Email Marketing Nerd 📧

Chase Dimond | Email Marketing Nerd 📧7 месяцев назад

This is awesome! So stoked about this!

Фото профиля InsForge

InsForge7 месяцев назад

Thank you! Let us know what you think :)

Фото профиля Shamim Hossain

Shamim Hossain7 месяцев назад

That’s impressive

Фото профиля InsForge

InsForge7 месяцев назад

You're impressive

Фото профиля Altiam Kabir

Altiam Kabir7 месяцев назад

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.

Фото профиля Nova IA

Nova IA7 месяцев назад

Very Nice ! Thanks insforge

Фото профиля InsForge

InsForge7 месяцев назад

🫡

Фото профиля Ava AI Labs

Ava AI Labs7 месяцев назад

Amazing !

Фото профиля InsForge

InsForge7 месяцев назад

thanks!

Фото профиля Yunhao Jiao

Yunhao Jiao7 месяцев назад

Incredible launch! Congrats~ 🚀

Фото профиля Dipti Sharma

Dipti Sharma7 месяцев назад

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

Фото профиля InsForge

InsForge7 месяцев назад

and cheaper to build ;)

Фото профиля Deborah

Deborah7 месяцев назад

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

Фото профиля Farhan Azad Shuvra

Farhan Azad Shuvra7 месяцев назад

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

Фото профиля Smiling Khan

Smiling Khan7 месяцев назад

Setting the benchmark and expectations very high

Фото профиля Marry Evan

Marry Evan7 месяцев назад

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

Фото профиля Fermionic

Fermionic7 месяцев назад

Go InsForge!🥰

Фото профиля InsForge

InsForge7 месяцев назад

Woohoo! 😁

Фото профиля - Elijah Muraoka -

- Elijah Muraoka -7 месяцев назад

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

Фото профиля InsForge

InsForge7 месяцев назад

@soshiapp LFG

Фото профиля The AI Guru 💡

The AI Guru 💡7 месяцев назад

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

Фото профиля Md Riyazuddin

Md Riyazuddin7 месяцев назад

Love seeing tools built specifically for agentic development.

Фото профиля InsForge

InsForge7 месяцев назад

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

Фото профиля Krishna Agrawal

Krishna Agrawal7 месяцев назад

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. ⭐

Фото профиля Selina

Selina7 месяцев назад

CONGRATS!!!!

Фото профиля Alejandro Martinez | IA

Alejandro Martinez | IA7 месяцев назад

This is very interesting

Фото профиля Salma ✨

Salma ✨7 месяцев назад

Awesome

Фото профиля Kylee

Kylee7 месяцев назад

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

Фото профиля InsForge

InsForge7 месяцев назад

that's the goal :) thanks!

Фото профиля Salma ✨

Salma ✨7 месяцев назад

That's very impressive

Фото профиля Keybzs

Keybzs7 месяцев назад

Great one

Фото профиля Emma Uses AI

Emma Uses AI7 месяцев назад

Cool !

Фото профиля Parul Gautam

Parul Gautam7 месяцев назад

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

Фото профиля Rony

Rony7 месяцев назад

Congratulations on the launch 👏

Фото профиля InsForge

InsForge7 месяцев назад

woohoo! 🎉

Фото профиля OtakuMachine

OtakuMachine7 месяцев назад

Nice @insforge

Фото профиля InsForge

InsForge7 месяцев назад

Nice 😎

Фото профиля VICK MELLY🇰🇪🇷🇺

VICK MELLY🇰🇪🇷🇺7 месяцев назад

I will check this one out

Фото профиля InsForge

InsForge7 месяцев назад

Let us know what you think!

Фото профиля 54NZ

54NZ4 месяцев назад

Cannot live a day without @insforge. I am hooked

Фото профиля InsForge

InsForge4 месяцев назад

🫶

Фото профиля Enzo Sanchez | IA

Enzo Sanchez | IA7 месяцев назад

Vamos @insforge !

Фото профиля InsForge

InsForge7 месяцев назад

vamos!

Фото профиля Mayank Agarwal 💡

Mayank Agarwal 💡7 месяцев назад

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

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Google AI

439,464 просмотров • 3 месяцев назад

.Decagon co-founder and CTO Ashwin Sreenivas says the choice between expensive frontier models and cheaper, less capable ones is a false trade-off: "Even if you have a 'dumber model,' you can get it to higher performance on that specific task." "When we fine-tune smaller, dumber models, it's that they're just not as general purpose, but on the specific task we want them to do, they actually outperform the large, smart, state-of-the-art models." "So we end up getting all three things. It is better at the task, it is cheaper, and it is faster." Ashwin Sreenivas

.Decagon co-founder and CTO Ashwin Sreenivas says the choice between expensive frontier models and cheaper, less capable ones is a false trade-off: "Even if you have a 'dumber model,' you can get it to higher performance on that specific task." "When we fine-tune smaller, dumber models, it's that they're just not as general purpose, but on the specific task we want them to do, they actually outperform the large, smart, state-of-the-art models." "So we end up getting all three things. It is better at the task, it is cheaper, and it is faster." Ashwin Sreenivas

a16z

36,067 просмотров • 2 месяцев назад

$Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.$

Google open-sourced MCP Toolbox for Databases. I gave it access to everything else. For context, Google's MCP Toolbox for Databases is an open-source server that lets AI agents securely query structured databases like PostgreSQL and MySQL through the MCP protocol However, most enterprise knowledge doesn't actually live in databases. It's scattered across emails, Slack threads, GitHub repos, Salesforce records, customer reviews, and internal docs. So Agents can't see any of it, which means they're working with a fraction of the context they need. I fixed that using MindsDB. It acts as a universal SQL layer that sits on top of all your data sources: structured, semi-structured, and unstructured. This means you can query Salesforce, Gmail, GitHub, S3 files, Jira, and 200+ more sources using SQL syntax. The clever part is how it connects to the MCP Toolbox. MindsDB exposes everything through MySQL, so from the Agent's perspective, it's just running SQL and getting context back. It doesn't know or care that the data came from five different sources behind the scenes. This setup unlocks some powerful capabilities: → One SQL interface for dozens of enterprise sources → Cross-datasource joins (combine GitHub and CRM data in a single query) → Built-in ML capabilities for working with unstructured data → Simple MCP tools that now have massively expanded reach In the video below, the Agent queries GitHub data and a customer review database in one SQL query. So what used to require ETL pipelines and weeks of engineering effort now happens instantly. At the end of the day, AI agents are only as useful as the data they can access. This gives them a lot more to work with. I have shared the GitHub repo in the replies, where you can find more details about this.

Akshay 🚀

40,562 просмотров • 8 месяцев назад

Our most intelligent workhorse model yet for coding and agents has arrived ⚡ Meet Gemini 3.7 Flash. — Crush that seemingly endless to-do list. Gemini Spark in the Google Gemini now uses 3.7 Flash. The new model can equip your personal AI agent to work even smarter for you by seamlessly handling complex, multi-step tasks across your Google Workspace apps like Gmail, Google Calendar and Google Docs — Enjoy a smoother build experience. The model thinks more diligently, putting more effort into multi-step planning and tool calls. A more disciplined execution means less manual oversight and fewer retries across engineering workflows — Build more, spend less. 3.7 Flash is available through the end of the year with an introductory price of half the original 3.6 Flash cost per million tokens ($0.75/1M input tokens and $3.75/1M output tokens)

Our most intelligent workhorse model yet for coding and agents has arrived ⚡ Meet Gemini 3.7 Flash. — Crush that seemingly endless to-do list. Gemini Spark in the Google Gemini now uses 3.7 Flash. The new model can equip your personal AI agent to work even smarter for you by seamlessly handling complex, multi-step tasks across your Google Workspace apps like Gmail, Google Calendar and Google Docs — Enjoy a smoother build experience. The model thinks more diligently, putting more effort into multi-step planning and tool calls. A more disciplined execution means less manual oversight and fewer retries across engineering workflows — Build more, spend less. 3.7 Flash is available through the end of the year with an introductory price of half the original 3.6 Flash cost per million tokens ($0.75/1M input tokens and $3.75/1M output tokens)

Google AI

129,191 просмотров • 2 месяцев назад

Last week $TEAM outlined a new AI bull case for SaaS, as a source of context to generate better, faster, cheaper AI outcomes. Atlassian demod how Claude Code by itself underperformed an identical instance of Claude Code, on an identical prompt, when the other instance had access to their Teamwork Graph (TWG) which has access to past project files, messages, documents, code bases, employee data, and system integrations (slack, outlook, etc). The added context pointed Claude to the right information to solve the task faster, whereas the instance without context thought more, read more, hallucinated, and got the task incorrect after spending 2x more tokens and running an extra minute. Since token spend is quickly becoming many multiples of traditional software cost for coding use cases (Jira at $10-12/seat vs Anthropic spend per seat in the several hundreds a month), Atlassian makes a compelling case that context pays for itself many times over by helping customers to get more value out of their expensive AI tokens. $MSFT easily has the strongest advantage as a source of differentiated context. Video worth watching.

Last week $TEAM outlined a new AI bull case for SaaS, as a source of context to generate better, faster, cheaper AI outcomes. Atlassian demod how Claude Code by itself underperformed an identical instance of Claude Code, on an identical prompt, when the other instance had access to their Teamwork Graph (TWG) which has access to past project files, messages, documents, code bases, employee data, and system integrations (slack, outlook, etc). The added context pointed Claude to the right information to solve the task faster, whereas the instance without context thought more, read more, hallucinated, and got the task incorrect after spending 2x more tokens and running an extra minute. Since token spend is quickly becoming many multiples of traditional software cost for coding use cases (Jira at $10-12/seat vs Anthropic spend per seat in the several hundreds a month), Atlassian makes a compelling case that context pays for itself many times over by helping customers to get more value out of their expensive AI tokens. $MSFT easily has the strongest advantage as a source of differentiated context. Video worth watching.

SouthernValue

31,196 просмотров • 5 месяцев назад

We need better transport and logistics. We need more capacity to move large volumes of goods. We need to move faster with the modernization of our roads and railways. We need military mobility that improves our defense capability. We need more storage facilities, more renewables, development of region’s critical minerals, stronger trade links and stronger manufacturing cooperation. We need to build more interconnectors between our energy systems. And of course we need to bring much more investment into the Carpathian region, which is a natural European gateway for routes coming from the South Caucasus and Asia through the Black Sea. These routes are active today, and they will remain active in the future. From the opening speech at the Inaugural C8 Summit in Ukraine. (2/3)

We need better transport and logistics. We need more capacity to move large volumes of goods. We need to move faster with the modernization of our roads and railways. We need military mobility that improves our defense capability. We need more storage facilities, more renewables, development of region’s critical minerals, stronger trade links and stronger manufacturing cooperation. We need to build more interconnectors between our energy systems. And of course we need to bring much more investment into the Carpathian region, which is a natural European gateway for routes coming from the South Caucasus and Asia through the Black Sea. These routes are active today, and they will remain active in the future. From the opening speech at the Inaugural C8 Summit in Ukraine. (2/3)

Volodymyr Zelenskyy / Володимир Зеленський

266,657 просмотров • 1 месяц назад