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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 -...

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InsForge

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1,592,962 görüntüleme • 7 ay önce •via X (Twitter)

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InsForge profil fotoğrafı
InsForge7 ay önce

Check out our repo:

InsForge profil fotoğrafı
InsForge7 ay önce

Try now:

Hitesh Choudhary profil fotoğrafı
Hitesh Choudhary7 ay önce

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 profil fotoğrafı
Imogen Ai7 ay önce

Amazing share, Let's connect for collaboration

Aivetra Maxine profil fotoğrafı
Aivetra Maxine7 ay önce

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

InsForge profil fotoğrafı
InsForge7 ay önce

AI mastered frontend, now we are helping it master backend

Chase Dimond | Email Marketing Nerd 📧 profil fotoğrafı
Chase Dimond | Email Marketing Nerd 📧7 ay önce

This is awesome! So stoked about this!

InsForge profil fotoğrafı
InsForge7 ay önce

Thank you! Let us know what you think :)

Shamim Hossain profil fotoğrafı
Shamim Hossain7 ay önce

That’s impressive

InsForge profil fotoğrafı
InsForge7 ay önce

You're impressive

Altiam Kabir profil fotoğrafı
Altiam Kabir7 ay önce

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 profil fotoğrafı
Nova IA7 ay önce

Very Nice ! Thanks insforge

InsForge profil fotoğrafı
InsForge7 ay önce

🫡

Ava AI Labs profil fotoğrafı
Ava AI Labs7 ay önce

Amazing !

InsForge profil fotoğrafı
InsForge7 ay önce

thanks!

Yunhao Jiao profil fotoğrafı
Yunhao Jiao7 ay önce

Incredible launch! Congrats~ 🚀

Dipti Sharma profil fotoğrafı
Dipti Sharma7 ay önce

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

InsForge profil fotoğrafı
InsForge7 ay önce

and cheaper to build ;)

Deborah profil fotoğrafı
Deborah7 ay önce

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

Farhan Azad Shuvra profil fotoğrafı
Farhan Azad Shuvra7 ay önce

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 profil fotoğrafı
Smiling Khan7 ay önce

Setting the benchmark and expectations very high

Marry Evan profil fotoğrafı
Marry Evan7 ay önce

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

Fermionic profil fotoğrafı
Fermionic7 ay önce

Go InsForge!🥰

InsForge profil fotoğrafı
InsForge7 ay önce

Woohoo! 😁

- Elijah Muraoka - profil fotoğrafı
- Elijah Muraoka -7 ay önce

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

InsForge profil fotoğrafı
InsForge7 ay önce

@soshiapp LFG

The AI Guru 💡 profil fotoğrafı
The AI Guru 💡7 ay önce

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

Md Riyazuddin profil fotoğrafı
Md Riyazuddin7 ay önce

Love seeing tools built specifically for agentic development.

InsForge profil fotoğrafı
InsForge7 ay önce

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

Krishna Agrawal profil fotoğrafı
Krishna Agrawal7 ay önce

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 profil fotoğrafı
Selina7 ay önce

CONGRATS!!!!

Alejandro Martinez | IA profil fotoğrafı
Alejandro Martinez | IA7 ay önce

This is very interesting

Salma ✨ profil fotoğrafı
Salma ✨7 ay önce

Awesome

Kylee profil fotoğrafı
Kylee7 ay önce

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

InsForge profil fotoğrafı
InsForge7 ay önce

that's the goal :) thanks!

Salma ✨ profil fotoğrafı
Salma ✨7 ay önce

That's very impressive

Keybzs profil fotoğrafı
Keybzs7 ay önce

Great one

Emma Uses AI profil fotoğrafı
Emma Uses AI7 ay önce

Cool !

Parul Gautam profil fotoğrafı
Parul Gautam7 ay önce

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

Rony profil fotoğrafı
Rony7 ay önce

Congratulations on the launch 👏

InsForge profil fotoğrafı
InsForge7 ay önce

woohoo! 🎉

OtakuMachine profil fotoğrafı
OtakuMachine7 ay önce

Nice @insforge

InsForge profil fotoğrafı
InsForge7 ay önce

Nice 😎

VICK MELLY🇰🇪🇷🇺 profil fotoğrafı
VICK MELLY🇰🇪🇷🇺7 ay önce

I will check this one out

InsForge profil fotoğrafı
InsForge7 ay önce

Let us know what you think!

54NZ profil fotoğrafı
54NZ4 ay önce

Cannot live a day without @insforge. I am hooked

InsForge profil fotoğrafı
InsForge4 ay önce

🫶

Enzo Sanchez | IA profil fotoğrafı
Enzo Sanchez | IA7 ay önce

Vamos @insforge !

InsForge profil fotoğrafı
InsForge7 ay önce

vamos!

Mayank Agarwal 💡 profil fotoğrafı
Mayank Agarwal 💡7 ay önce

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

Benzer Videolar

Introducing InsForge Backend Branching. We made agentic coding production-safe. Your agent creates isolated backend branches when working. Every backend service, policy, and config is copied into a secure, isolated environment. Review PR-style diffs. Merge only when ready.
1:33

Introducing InsForge Backend Branching. We made agentic coding production-safe. Your agent creates isolated backend branches when working. Every backend service, policy, and config is copied into a secure, isolated environment. Review PR-style diffs. Merge only when ready.

InsForge

62,391 görüntüleme • 4 ay önce

Introducing Diagnose and Debug. Give your agent first-party backend context: health metrics, logs, errors, database checks, and more. Your coding agent catches backend issues now, then fixes them by working with your dedicated InsForge debugging agent. Agent <> Agent.
1:50

Introducing Diagnose and Debug. Give your agent first-party backend context: health metrics, logs, errors, database checks, and more. Your coding agent catches backend issues now, then fixes them by working with your dedicated InsForge debugging agent. Agent <> Agent.

InsForge

53,090 görüntüleme • 4 ay önce

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.
0:51

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.

Avi Chawla

65,041 görüntüleme • 1 ay önce

We’re building the agent-native alternative to AWS, but it's actually good. - 33,000+ projects created (10% WoW growth) - 2x weekly active projects (2000 → 4000+) - 11,000+ GitHub stars (5x in the last 3 months) - faster than React, Supabase, and Linux Introducing InsForge ↓
1:42

We’re building the agent-native alternative to AWS, but it's actually good. - 33,000+ projects created (10% WoW growth) - 2x weekly active projects (2000 → 4000+) - 11,000+ GitHub stars (5x in the last 3 months) - faster than React, Supabase, and Linux Introducing InsForge ↓

InsForge

603,836 görüntüleme • 4 ay önce

Introducing InsForge Compute. Deploy long-running backend services as Docker containers via your coding agent. It writes the Dockerfile, builds the image, injects env vars, runs the container, and returns a live URL. All in under 60 seconds.
1:21

Introducing InsForge Compute. Deploy long-running backend services as Docker containers via your coding agent. It writes the Dockerfile, builds the image, injects env vars, runs the container, and returns a live URL. All in under 60 seconds.

InsForge

1,511,264 görüntüleme • 4 ay önce

Today we’re launching 4 open-source Next.js app templates: 1. Basic starter 2. Chatbot 3. CRM 4. Ecommerce We made building real full-stack apps even easier on InsForge. Try one in your next project: npx @insforge/cli create Thread below with all 4 repos ↓
1:00

Today we’re launching 4 open-source Next.js app templates: 1. Basic starter 2. Chatbot 3. CRM 4. Ecommerce We made building real full-stack apps even easier on InsForge. Try one in your next project: npx @insforge/cli create Thread below with all 4 repos ↓

InsForge

671,992 görüntüleme • 6 ay önce

Introducing InsForge Payments. Your coding agent can now set up Stripe payments for your app using InsForge. Describe your pricing and watch it create products and prices in Stripe, add checkout, and set up billing portal sessions. Test first. Go live when ready.
1:16

Introducing InsForge Payments. Your coding agent can now set up Stripe payments for your app using InsForge. Describe your pricing and watch it create products and prices in Stripe, add checkout, and set up billing portal sessions. Test first. Go live when ready.

InsForge

674,924 görüntüleme • 4 ay önce

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.
0:29

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.

Avi Chawla

113,330 görüntüleme • 4 ay önce

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.
0:22

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.

Akshay 🚀

123,101 görüntüleme • 4 ay önce

Introducing Coldtea. The ADE for shipping self-driving software. Purpose-built for the entire software lifecycle. Other IDEs optimise for one thing: Build. Run more agents in parallel, ship faster, orchestrate better. But software engineering is not just about building. Coldtea AI brings your coding agents, agentic end-to-end testing, and production monitoring agents into ONE development environment, so you can move at agent speed without breaking anything.
1:00

Introducing Coldtea. The ADE for shipping self-driving software. Purpose-built for the entire software lifecycle. Other IDEs optimise for one thing: Build. Run more agents in parallel, ship faster, orchestrate better. But software engineering is not just about building. Coldtea AI brings your coding agents, agentic end-to-end testing, and production monitoring agents into ONE development environment, so you can move at agent speed without breaking anything.

Ohans Emmanuel

441,533 görüntüleme • 2 ay önce

Introducing 🦀 CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents 🦀 CRAB provides an end-to-end and easy-to-use framework to build multimodal agents, operate environments, and create benchmarks to evaluate them, featuring three key components: - 🔀 Cross-environment support - agents can operate tasks in 📱 Android and 💻 Ubuntu. - 🕸️ Graph evaluator - provides a fine-grain evaluation metric for agents. - 🤖 Task generation - composes subtasks to automatically generate tasks. By connecting all devices to agents, 🦀CRAB unlocks greater capabilities for human-like tasks than ever before. Use 🦀 CRAB to benchmark your multimodal agents! - 👨‍💻 Check out the repository: - 📝 Read the paper: - 🌐 Find out more via the project page: - 🐫 Join our community:
1:05

Introducing 🦀 CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents 🦀 CRAB provides an end-to-end and easy-to-use framework to build multimodal agents, operate environments, and create benchmarks to evaluate them, featuring three key components: - 🔀 Cross-environment support - agents can operate tasks in 📱 Android and 💻 Ubuntu. - 🕸️ Graph evaluator - provides a fine-grain evaluation metric for agents. - 🤖 Task generation - composes subtasks to automatically generate tasks. By connecting all devices to agents, 🦀CRAB unlocks greater capabilities for human-like tasks than ever before. Use 🦀 CRAB to benchmark your multimodal agents! - 👨‍💻 Check out the repository: - 📝 Read the paper: - 🌐 Find out more via the project page: - 🐫 Join our community:

CAMEL-AI.org

72,807 görüntüleme • 2 yıl önce

Today we're launching Airbyte Agents. It’s a platform meant to solve what we call the discovery gap. Agents in demos are successful because they know the fields and sources they have to call to solve their task. Agents in production fail because they don’t have that knowledge assembled beforehand. Airbyte Agents, powered by the Context Store, fixes that with one unified layer of pre-materialized context across all of your business systems. Real results. 80% fewer tokens. 40% fewer tool calls. Get more out of your AI tool’s plan limit. Three ways to build: MCP, SDK, or no-code Automations 💡 We're early, and we want to build with our users. Try Airbyte Agents today.
2:51

Today we're launching Airbyte Agents. It’s a platform meant to solve what we call the discovery gap. Agents in demos are successful because they know the fields and sources they have to call to solve their task. Agents in production fail because they don’t have that knowledge assembled beforehand. Airbyte Agents, powered by the Context Store, fixes that with one unified layer of pre-materialized context across all of your business systems. Real results. 80% fewer tokens. 40% fewer tool calls. Get more out of your AI tool’s plan limit. Three ways to build: MCP, SDK, or no-code Automations 💡 We're early, and we want to build with our users. Try Airbyte Agents today.

Airbyte

6,762,384 görüntüleme • 5 ay önce

Glen is building the organizational learning and context layer for agents. Every agent session your team runs produces reasoning worth keeping, and nearly all of it is discarded the moment the window closes. Glen is the layer that unifies every agent and model your company run. Glen pulls in agent sessions from Claude Code, Codex and Cursor, plus Slack messages, GitHub prs, tickets, docs and calls. When a new agent picks up a task, Glen finds relevant prior context and injects it in the agents context window to steer it, producing knowledge work that is significantly higher quality, faster and cheaper than ever before. 29% less context and 21% faster to be exact. And Glen does more... > query the original agent behind any line of code > search every session your org has ever run > dynamic skill distillation > multiplayer agents > unified transcripts you can hand between Claude Code, Codex, and Cursor mid-task, > a company brain in Slack. Glen is in early access, sign up for the waitlist below.
2:29

Glen is building the organizational learning and context layer for agents. Every agent session your team runs produces reasoning worth keeping, and nearly all of it is discarded the moment the window closes. Glen is the layer that unifies every agent and model your company run. Glen pulls in agent sessions from Claude Code, Codex and Cursor, plus Slack messages, GitHub prs, tickets, docs and calls. When a new agent picks up a task, Glen finds relevant prior context and injects it in the agents context window to steer it, producing knowledge work that is significantly higher quality, faster and cheaper than ever before. 29% less context and 21% faster to be exact. And Glen does more... > query the original agent behind any line of code > search every session your org has ever run > dynamic skill distillation > multiplayer agents > unified transcripts you can hand between Claude Code, Codex, and Cursor mid-task, > a company brain in Slack. Glen is in early access, sign up for the waitlist below.

Nikos

20,466 görüntüleme • 2 ay önce

Long, long, ago, coding agents could do nothing but write code Forger was powerless, just like all the others. Claude Code. Codex. Cursor. Until one day... InsForge Launch Week 2 starts Monday, June 1st
0:24

Long, long, ago, coding agents could do nothing but write code Forger was powerless, just like all the others. Claude Code. Codex. Cursor. Until one day... InsForge Launch Week 2 starts Monday, June 1st

InsForge

35,357 görüntüleme • 4 ay önce

Today, we're introducing not one but TWO new models, striking the balance between efficiency and quality to enable you to build production AI agents. — Gemini 3.6 Flash: Addresses efficiency feedback we received from Gemini 3.5 Flash with upgrades in coding, knowledge work, and multimodal tasks faster, more accurately, and with substantially fewer tokens per task — Gemini 3.5 Flash-Lite: Our fastest, most cost-effective 3.5-class model yet built for agentic workflows, hitting ~350 output tokens/sec with improved coding and overall quality Start building with these today via the Gemini API in Google AI Studio or try them out in the Google Gemini
0:42

Today, we're introducing not one but TWO new models, striking the balance between efficiency and quality to enable you to build production AI agents. — Gemini 3.6 Flash: Addresses efficiency feedback we received from Gemini 3.5 Flash with upgrades in coding, knowledge work, and multimodal tasks faster, more accurately, and with substantially fewer tokens per task — Gemini 3.5 Flash-Lite: Our fastest, most cost-effective 3.5-class model yet built for agentic workflows, hitting ~350 output tokens/sec with improved coding and overall quality Start building with these today via the Gemini API in Google AI Studio or try them out in the Google Gemini

Google AI

439,536 görüntüleme • 3 ay önce

.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
0:47

.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 görüntüleme • 2 ay önce

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.
0:22

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 görüntüleme • 8 ay önce

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)
1:15

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 görüntüleme • 2 ay önce

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.
4:52

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 görüntüleme • 5 ay önce

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)
0:56

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 görüntüleme • 1 ay önce