Entelligence AI (@EntelligenceAI) - Twitter Profili

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The next wave isn’t about writing better code. It’s about giving engineering leaders the intelligence to ship it. Who’s optimizing AI spend? Who’s measuring outcomes? Who’s making sure AI-generated code actually reaches production? These are the questions we’ve been building to answer. Over the next few weeks, we’ll be launching Model Router, Agent Insights, CLI, and more to help engineering teams optimize AI spend, measure what their agents actually deliver, and ship with confidence. New launches. Every week. Stay tuned.

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Entelligence AI@EntelligenceAI·7h

@adxtyahq As local models keep improving, intelligent routing across local and cloud inference is only going to become more important

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aditya@adxtyahq·7h

didn't expect to see a local inference setup outperform the official API of the same model by 2.2x they ran Hy3 locally on 4× RTX 5090s with 128GB VRAM and compared it against the official API using the same prompts to build Flappy Bird, Arkanoid, and Snake. the local run generated ~77K tokens in 15.5 minutes, while the API generated ~75K tokens in 34.3 minutes with nearly identical outputs. local AI has been improving ridiculously fast lately, getting increasingly bullish on local inference.

atomic.chat@atomic_chat_hq

1-bit Hy3 running locally is 2.2x faster than its API at the same quality! We gave both models the same task and compared one-shot outputs. 1-bit Hy3 295B GGUF (92GB) ran locally on 4x RTX 5090 with 128GB VRAM against the same Hy3 over cloud API Tasks: - Flappy Bird - Arkanoid - Snake Outputs: Hy3 1-bit local: 76.9K tokens, 15.5 min Hy3 cloud API: 75.1K tokens, 34.3 min The 1-bit games look the same as the API ones. Birds fly through the pipes, bricks break, the snake eats and grows. Nothing froze or crashed. Both models even made the same slip: the snake can cross itself and the game does not end. Getting this quality from 1 bit running locally is wild! Run Hy3 GGUF yourself in Atomic Chat in 2 clicks

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Entelligence AI@EntelligenceAI·11h

If you're trying to keep up with all these launches, we've been publishing detailed breakdowns Latest: GPT-5.5 vs GLM-5.2: Is Higher Performance Worth the Extra Cost? - entelligence.ai/blogs/gpt-5.5-…

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Entelligence AI@EntelligenceAI·12h

🚨 Kimi K3 is set to launch within hours And this could be one of the most important open-model launches of the year. Why people are paying attention: • K2.6 was already one of the strongest agentic models available • Open-weight • 256K context • Native vision • Thousands of tool calls in long-running sessions • Up to 300 coordinated sub-agents • ~7× cheaper than Claude Opus at current OpenRouter pricing • 386B+ tokens processed through OpenRouter Now K3 arrives with: • A new architecture (not just a larger K2.6) • K3 Agent Swarm for large-scale parallel search and execution • Focus on long-horizon agent workflows • Stronger coding and research capabilities • Launch across app, desktop, CLI, and API Moonshot appears to be betting that the future isn't a single model. It's an army of agents working together. The biggest question: Can K3 challenge GLM-5.2, GPT-5.6, and Claude Fable 5 in real-world agentic workflows? We're about to find out.

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Entelligence AI@EntelligenceAI·19h

The biggest surprise from the GPT-5.6 benchmarks isn't Sol. It's Luna. GPT-5.6 Luna: • Matches GPT-5.5 xHigh at roughly half the cost • Outperforms Fable 5 Medium at roughly half the cost • Sits directly on the Pareto frontier • Hits 73% on DeepSWE leaderboard family results Meanwhile Sol takes the top spot for raw performance. The emerging pattern: Luna for value. Sol for maximum intelligence. And Terra?

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Entelligence AI@EntelligenceAI·20h

@adxtyahq 😂

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aditya@adxtyahq·21h

OpenAI waking up and choosing violence against Anthropic

Tibo@thsottiaux

Tomorrow might be 8M active user celebration day. Just saying

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Entelligence AI@EntelligenceAI·21h

China may have just nuked an entire AI infrastructure category. Tencent open-sourced Agent Memory. A long-term memory system for AI agents that: • Runs locally • Uses SQLite • Requires no vector database • Requires no cloud APIs • Cuts token usage by 61% Instead of turning your history into an opaque vector blob, it builds a semantic hierarchy: L0 → Conversations L1 → Facts L2 → Scenarios L3 → Persona Readable. Inspectable. Debuggable. The vector database industry should be paying attention.

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Entelligence AI@EntelligenceAI·20h

@adelbucetta 💯

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Adel Bucetta@adelbucetta·21h

@EntelligenceAI that's a massive unlock for medical research

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Entelligence AI@EntelligenceAI·1d

A healthcare-focused model just beat frontier models on some of the hardest medical benchmarks. Cura 1T: • Beats GPT-5.5 on HealthBench Hard • Beats Claude Fable 5 on HealthBench Professional • Beats Opus 4.8 on AgentClinic • Beats Opus 4.8 on MedAgentBench-v2 The interesting part? It wasn't trained conventionally. It was trained using recursive self-improvement (RSI): AI trains AI. AI finds failures. AI generates new training data. Humans approve or reject each iteration. This is interesting because we're starting to see domain-specific models outperform general frontier models in their own territory.

Weiran Yao@iscreamnearby

The strongest healthcare LLM, custom-built for your enterprise, owned by you🌸 Meet @actAVAai Cura: 1T agentic model trained by recursive self-improvement for long clinical + health admin workflows Try: actava.ai/cura Share your use case: $20 credits + early access👇

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Entelligence AI@EntelligenceAI·1d

The AI race isn't slowing down, it's changing At the beginning of 2026 everyone was obsessed with bigger models, longer context windows, and generating more code. Today the biggest challenge is keeping AI spend flat as usage keeps exploding Brian Armstrong recently highlighted routing as one of the biggest levers behind this shift That's exactly why we built Entelligence Model Router, same output, smarter routing, lower AI spend

Entelligence AI@EntelligenceAI

The next wave isn’t about writing better code. It’s about giving engineering leaders the intelligence to ship it. Who’s optimizing AI spend? Who’s measuring outcomes? Who’s making sure AI-generated code actually reaches production? These are the questions we’ve been building to answer. Over the next few weeks, we’ll be launching Model Router, Agent Insights, CLI, and more to help engineering teams optimize AI spend, measure what their agents actually deliver, and ship with confidence. New launches. Every week. Stay tuned.

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Entelligence AI@EntelligenceAI·1d

@asaravu_ 🚀🚀🚀

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adithya@asaravu_·1d

mega launch week coming up!! @EntelligenceAI HQ goal: teams should be able to ship enterprise grade code without burning through their treasuries on tokens.

San Francisco, CA 🇺🇸 English

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Entelligence AI@EntelligenceAI·1d

@RainmanJam yeah. they are giving $20 credits to try. let me check

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Shannon Atkinson@RainmanJam·1d

@EntelligenceAI This is huge, but the real challenge lies in integration. A model can outperform others in benchmarks, but if it can't fit seamlessly into existing healthcare workflows, it’s not going to make the impact we need.

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Entelligence AI@EntelligenceAI·1d

incase you dont know about what we are launching soon, do checkout - x.com/EntelligenceAI…

Entelligence AI@EntelligenceAI

The AI race isn't slowing down, it's changing At the beginning of 2026 everyone was obsessed with bigger models, longer context windows, and generating more code. Today the biggest challenge is keeping AI spend flat as usage keeps exploding Brian Armstrong recently highlighted routing as one of the biggest levers behind this shift That's exactly why we built Entelligence Model Router, same output, smarter routing, lower AI spend

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Aiswarya Sankar@Aiswarya_Sankar·1d

The @EntelligenceAI launch week is kicking off! Full week of launches and updates coming out daily Helping teams ship higher quality code without burning through their entire company savings

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aditya@adxtyahq·1d

At the beginning of 2026 everyone was obsessed with shipping more AI generated code and optimizing token usage Now every engineering leader is asking a different question: is any of this actually helping us ship better That’s exactly what we’re building at Entelligence

Entelligence AI@EntelligenceAI

The next wave isn’t about writing better code. It’s about giving engineering leaders the intelligence to ship it. Who’s optimizing AI spend? Who’s measuring outcomes? Who’s making sure AI-generated code actually reaches production? These are the questions we’ve been building to answer. Over the next few weeks, we’ll be launching Model Router, Agent Insights, CLI, and more to help engineering teams optimize AI spend, measure what their agents actually deliver, and ship with confidence. New launches. Every week. Stay tuned.

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Entelligence AI

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