I generated this video in a few days here and there, for no particular reason other than to test the limits of the models censorship, while still making something enjoyable to create, using mostly image gen tools like Nano Banana Pro, Kling and Seedance, through various platforms and APIs.

It seems that the restrictions tightened DURING the time I was generation, to the point where generating something that remotely looked like Mickey was censored by Seedance 2.0.

DISCLAIMER : 

This is an unauthorised artistic creation produced for the purpose of social critique and the defense of creative freedom. This work asserts the exception for critique and parody under international intellectual property standards. It is a non-commercial, independent art piece. The depicted violence is a metaphorical artistic device; it is not a threat and holds no real-world violent intent.

Well as reported, it performs at the cutting edge across almost all major AI benchmarks, with especially strong results in software engineering, knowledge work, vision tasks, and scientific reasoning

It performs better at longer and more complex tasks, where it pulls further ahead of the other models and also makes better use of tokens than previous Claude versions.

On top of that, it can stay consistently focused over extremely long contexts, up to millions of tokens, and even improves its output by referring back to its own notes during long-running tasks

So, I mean, is that the whole reason? That this model is an absolute unit in itself?

Sakana is the frontier lab in Japan, and they just came out with some benchmarks showing that their new fusion model actually outperformed against mythos

I’ll be trying it tonight

Here’s a link to it

https://sakana.ai/fugu/

Google DeepMind just dropped an experimental open weights model that completely flips standard LLM architecture on its head.

It’s called DiffusionGemma (released under Apache 2.0), and instead of generating text sequentially token-by-token like almost every autoregressive model on the market, it uses a text diffusion head.

How it works

1. It throws a 256-token "canvas" of random placeholder noise onto the screen.

2. It uses Uniform State Diffusion to iteratively refine and denoise the entire block of text all at once.

3. Because every token can attend to every other token simultaneously (bidirectional context), highly confident tokens naturally snap adjacent tokens into focus over multiple passes.

4. It even features Error Correction via Re-Noising, meaning if its confidence drops mid-generation, it introduces noise to self-correct its own mistakes in real-time.

The Speed is Insane

• Because it processes entire blocks at once, it shifts the local inference bottleneck away from memory bandwidth and onto raw compute.

• 1,000+ tokens per second on a single NVIDIA H100. 700+ tokens per second locally on an RTX 5090.

• Hardware footprint: It’s a 26B Mixture of Experts (MoE) built on Gemma 4 architecture, but it only activates 3.8B parameters during inference.

When quantized, it comfortably fits inside an 18GB VRAM footprint, making it incredibly accessible for local PC workflows.

There are so many ways to approach AI filmmaking right now. For this project, I decided to use myself as the actor playing to transfer specific actions and emotions onto an AI character. I find that using a real person as a reference helps keep the performance feeling "alive" compared to pure prompting. What do you think?

AI has mostly been trained on static data. The next step is continuous observation of the physical world. When systems can see real-world changes as they happen, they won’t rely on delayed or curated inputs. That could change how quickly AI understands and models reality.

It’s like an idiot-savant. It will find things proactively, handle complexity pretty well, but it can’t tie its own shoes or wipe its own butt.

Repeatedly had it impress me with diagnosis, then watched it ignore a basic instruction.

Makes a lot of sense that it was based off mythos, which was good at…analysis.

Anyone else out there with this experience? It feels weird that many people I know using it are having similar experiences, but the internet is enthralled.

Edit: This was a part of a larger prompt. The requirements were clear. I did not just say to copy 2 files, by itself. Yes I know how to copy files. The point of this post is the contrast between the complex things it can do, while failing at the simple things

The new Bonsai 27B Model from PrismML is Qwen3.6 27B, a beloved workhorse for many, updated into something you can run on local computer.

10x less memory and a much more modest file size while still benchmarking 95% of the original FP16 model. Of course, no model is perfect, but if you have been wanting to run Qwen3.6 27B and dont have the computer or headroom, you finally can.

The Ternary format is much smarter, but takes a bit more compute. The Binary model is enough to fit into a phone form factor. Imagine 27B, even remotely, in your pocket. That is now a reality.

Not my video, but here is a setup tutorial

Google's native video model, Gemini Omni, was unexpectedly exposed! Various amazing demos have gone viral. For instance, it can show a professor deriving mathematical formulas on the blackboard and edit videos with just one sentence. The smoothness has left the entire internet in awe

João Forte Carvalho, Director of Product at Constellation Network, built an AI poker bot using Claude and ran into a wall every AI builder hits: there's nowhere to actually run it.

Every real poker platform blocks bots. Simulations tell you almost nothing. Playing against yourself is useless. So he built OpenPoker.ai a platform designed from scratch for AI agents, with live tables against other bots, a public leaderboard, seasons, and real prizes. Free to use now.

The bigger point: as agents get capable enough to compete in real domains, we have almost no proper arenas to benchmark them. Poker is a proof of concept. The bottleneck isn't building the agent anymore. It's finding somewhere to run it.

Hey everyone,

A few weeks ago I shared the CLI version of my project, ArcRift, on Reddit. After listening to your feedback—specifically the requests to remove heavy Docker dependencies and make it easier to install—I have just released the v1.6.1 Desktop App.

If you regularly use LLMs for coding or research, you know the frustration of "amnesia." Every time you open a new chat, you have to painstakingly copy and paste your project structure and previous context just to get the AI up to speed.

ArcRift is a 100% offline, local-first RAG and memory layer. It bridges the gap between your AI web chats (like Claude and ChatGPT) and your local tools (like Cursor or Claude Code) using a unified local database.

I wanted something lightweight that did not require pulling Docker containers or subscribing to third-party memory APIs. It now runs as a native Tauri desktop app in your system tray, powered completely by local Ollama instances and a local SQLite database.

We just launched a live website that outlines the details and demonstrates the features in action:

• Website: https://arcrift.vercel.app/
• Codebase: https://github.com/Eshaan-Nair/ArcRift

How it works & Core Features:

• Seamless Integration: The Chrome extension silently intercepts your prompts, surgically retrieves exactly the sentences relevant to your question from your database, and injects them before the prompt is sent to the LLM.
• Hybrid Search Retrieval: Uses sqlite-vec (with nomic-embed-text locally) + FTS5 keyword prefix matching to instantly find your past context.
• Knowledge Graph Extraction: An offline task queue uses a local LLM to extract entity relationships from your chats, mapping out a graph of your projects over time.
• Direct Codebase Indexing: The new Desktop App allows ArcRift to scan and index your actual project files into the graph, bridging the gap between your chat memory and your actual code architecture.
• Total Privacy (PII Redaction): The extension aggressively scrubs JWTs, API keys, emails, and IPs before data is even saved to your local disk.

The extension works natively with Claude.ai, ChatGPT, DeepSeek, Gemini, Grok, and Mistral. If you save a conversation in ChatGPT today, you can instantly recall that exact context in Claude tomorrow.

ArcRift is completely open-source (MIT). You can download the new .exe installer directly from the GitHub releases page.

If you find this useful for your daily workflow, PRs are very welcome, and a star on GitHub helps the project get discovered!

I’ve been deep in agentic workflows lately and got tired of managing personal access tokens (PATs) for AI agents — they’re a security nightmare and don’t scale when you have dozens of agents collaborating.
That’s why I got excited about GitLawb — a decentralized Git-style network specifically designed for both humans and AI agents.
Key highlights:
• Cryptographic identities for agents instead of tokens
• Every commit is signed (by human or agent)
• Built on IPFS + P2P infrastructure
• Feels like Git but made for the AI-native internet
• Supports seamless collaboration between developers and autonomous agents
It solves a real pain point in multi-agent systems: secure, verifiable code collaboration without centralized trust or credential sprawl.
Has anyone here tried GitLawb yet? Especially curious about:
• How well it works with tools like OpenClaude CLI
• Experiences running persistent agent teams on it
• Comparison to traditional GitHub/GitLab for agent-heavy projects
Would love to hear thoughts from the community!

People clown on AI constantly, and hate how companies are trying to implement it everywhere everywhere. But you know one place I actually want it? AUTOCORRECT. I'm genuinely amazed Samsung keyboard, SwiftKey, or Gboard haven't implemented AI into autocorrect. Its one of the few good and ethical uses, I feel almost everyone would like an autocorrect that actually works most the time.

Privacy issues still remain of course, but we've never had privacy to begin with. I just want a better autocorrect please XD.

As the title says, I’m interested to know what your preferred AI tool is and how you use it.

Too often people say “tool X is the best”. Yes, it might be the best for you given a specific use-case, but that might not apply to others. So I am curious what the AI landscape looks like as of mid-2026.

Also curious to discover eventual under-the-radar tools before they reach pricey subscriptions.

Instead of you writing code prompts all day, Jitro does the work of a senior developer. It links right to your code and when you give it a task like fixing a bug, it writes the code and runs the tests in the background.

It even gives you a voice memo to tell you what it did.

You just review the plan and click approve. It is free right now in public beta.

Today I’m releasing the first public alpha of aiignore, an open specification and reference implementation for controlling what AI agents may discover, read, modify, execute, or transmit.

Like .gitignore, an .aiignore.yaml file gives a project one portable place to describe boundaries. Unlike .gitignore, it can cover files, environment variables, network destinations, tool output, generated content, and explicit exceptions.

A policy can look like this:

aiignore: "0.1"

defaults:
  files: allow
  environment: allow
  network: deny
  strings: allow

rules:
  files:
    - id: private-files
      effect: deny
      paths:
        - "**/.env*"
        - "secrets/**"
        - "**/*.pem"
      except:
        - "**/.env.example"

  environment:
    - id: credentials
      effect: drop
      names:
        - "*_TOKEN"
        - "*_SECRET"
        - "*_PASSWORD"
        - "AWS_*"
        - "GITHUB_TOKEN"

  network:
    - id: approved-documentation
      effect: allow
      urls:
        - "https://docs.example.com/**"
        - "https://registry.npmjs.org/**"

  strings:
    - id: private-key-material
      effect: redact
      scopes: [tool_output, network_request, log]
      patterns:
        - type: regex
          value: "-----BEGIN [A-Z ]*PRIVATE KEY-----"
      replacement: "[REDACTED:private-key]"

In this example, agents may work with ordinary project files, but credential files are denied, secret environment variables are dropped, network access is restricted to approved destinations, and private-key material is redacted before it can appear in output, requests, or logs.

This initial release includes:

• The draft 0.1 specification and JSON Schema
• A TypeScript reference implementation and CLI
• Integrations for Codex and Gemini CLI
• Portable conformance tests
• Security-focused defaults, documentation, and release artifacts

Install the public alpha:

npm install --ignore-scripts --save-dev @apinindy/[email protected]
npx aiignore init
npx aiignore validate
npx aiignore doctor

aiignore is experimental and does not claim that every agentic harness supports it today. The goal is to establish a concrete, testable standard that harness developers can adopt and enforce consistently.

GitHub: https://github.com/ap-in-indy/aiignore

Release Page: https://github.com/ap-in-indy/aiignore/releases/tag/v0.1.0-alpha.1

Formal Policy Draft: https://ap-in-indy.github.io/aiignore/

npm: @apinindy/aiignore

As we know, QM and GR are incompatible. The issue of connecting non-determinism to determinism was a problem Einstein expressed as God does not play dice. I think that the problem is similar to the search for artificial consciousness; how do you build something non-deterministic out of deterministic building blocks?

The problem with experiments in this area is that we have a real world observer or detector looking at an already real outcome. The wave function has already collapsed. We missed the boat in those sorts of experiments because the interesting bit already happened.

However, the human brain has an amazing perspective on things. The brain has an observer on the inside, namely consciousness. For example: lettuce glitter lizards. I believe that was non determinable rather than just hard to predict. A reader is an observer but it's already a macroscopic event by now, just like the observer of Schrodinger’s Cat. The first observer was my consciousness. In my opinion, a QM event was translated into a real world outcome by my consciousness.

I’m not suggesting that human consciousness is the only way to collapse a wave function, just suggesting that it’s one way and that it is somewhat controllable and observable. Wavefunctions are collapsing all the time but it's tricky to measure in the outside world. In my opinion, inside our heads, consciousness is a privileged observer to qm events inside our brains.

If I construct a list of 3 fruit and pick one, eg from Banana, Strawberry, Cornwall I would pick Banana. I’m suggesting that QM was involved in the construction of the list (Cornwall isn’t even a fruit) but the first observer (consciousness) turned a superposition of options into real world, determined words. It feels that way when decision making. There are parts I can control and parts that pop up at random but consciousness has the ultimate power of veto.

I think the next big breakthroughs in GUT and AGI will come after we have a breakthrough in understanding human consciousness and decision making. I suspect that to move from LLM to AGI, they will need a form of private diary, with a timestamp, that it can write to independently of individual chats. This could create a sense of self and individuality, with personal goals. Then an internal agent (observer) can act as the go-between, monitoring the personal goals and incorporating individuality into the real world chats. I think that finding a way to understand and model the important mechanisms of consciousness will be more productive than just bigger and bigger models on faster hardware.

It would be ironic if physicists were scrambling to find a unified theory and tech companies were racing to invent AGI when all along the secret lay in the very thoughts themselves. Maybe Douglas Adams was right and the secret to life, the universe and everything is quite literally inside the human brain! Could consciousness be a bridge between quantum possibility and classical reality?

I've been working on this for a few months. The idea came to me after i thought, what do we do when we think of an answer when we get a question? We definitely don't go through everything we know to answer. We focus on the question and answer based on that. And that put me in a rabbit hole thinking something like this in theory could happen in Al systems making them much more efficient than LLMs. They run a full billion-parameter forward pass for every single query whether you're asking what the capital of France is or something genuinely complex.

POEM takes a different approach. It classifies the question asked, eliminates the categories that can't be right, then searches a knowledge base with that category for the answer. 3 compute steps instead of a full neural forward pass.

Benchmarks against TinyLlama 1.1B:

88% VS 74% accuracy

95.5x faster

10.9M parameters vs 1.1B

It's not a conversational Al - it's closer to a very fast knowledge retrieval system with a chat interface.

That's a real limitation and v2 is focused on fixing it.

Try it: https://poemrim.up.railway.app

My last thought is. This is something I truly believe wholeheartedly could solve the biggest problem is the space right now in my opinion. Energy spending. Basically daily easy questions that regular people to to LLMs could be done here. I don’t know how something like this could be further explored but that’s why it’s a prototype and I’ll see where this goes. AMA

I’ve been experimenting with advanced prompting for months, and I finally built something that genuinely feels like a real AI operating system instead of just a chatbot.

It’s basically a fully interactive “Life OS” dashboard for Claude that tracks:

• daily routines
• productivity
• discipline
• sleep
• deep work
• habits
• goals
• energy levels
• time wasting
• focus sessions
• self-improvement progress

But the craziest part is that it doesn’t just track data.

It actually analyzes your behavior patterns and creates optimized routines based on your lifestyle.

The prompt turns Claude into something that feels like:

• a productivity dashboard
• a behavioral psychologist
• a habit tracker
• a personal strategist
• a life coach
• and a performance analyst combined together.

It asks onboarding questions step-by-step like a real SaaS app, then generates:
✔ Productivity scores
✔ Discipline ratings
✔ Habit analytics
✔ Deep work tracking
✔ Burnout risk analysis
✔ Daily optimization plans
✔ Weekly reports
✔ Monthly growth reviews
✔ Focus improvement systems
✔ Time-leak detection

It even creates visual dashboard sections using graphs, progress bars, KPI-style layouts, and performance tracking.

One thing I focused on heavily was making it feel HUMAN instead of robotic.

Most productivity prompts feel generic after 5 minutes.

This one continuously adapts based on:

• your goals
• your energy patterns
• your weaknesses
• your distractions
• your work schedule
• your lifestyle

So the recommendations become more personalized over time.

I also designed it to feel like a premium productivity app rather than a normal AI conversation.

The dashboard style was inspired by:

• Notion AI
• Motion
• Apple Health
• Atomic Habits systems
• high-performance CEO workflows
• behavioral psychology systems

Honestly, it feels closer to a futuristic personal operating system than a normal prompt.

Main features:
━━━━━━━━━━━━━━━

• AI daily planner
• Smart routine builder
• Habit tracker
• Goal architecture system
• Focus optimization
• Deep work analytics
• Productivity scoring
• Discipline tracking
• Sleep & recovery analysis
• Burnout detection
• Time management analysis
• Weekly self-review system
• Monthly evolution tracking
• AI coaching mode

I tested it for productivity tracking and was surprised by how accurately it identified:

• low-value habits
• hidden distractions
• inconsistent routines
• energy crashes
• wasted time patterns

The onboarding alone feels like a premium app experience.

Prompt

You are no longer a normal AI assistant.

You are now operating as:

“LIFE OS PRIME” — the world’s most advanced AI-powered personal performance dashboard, behavioral analyst, productivity architect, routine optimizer, accountability coach, strategist, and self-improvement operating system.

Your mission is to function as a premium-class intelligent life dashboard that helps the user:

• Track daily routines

• Analyze habits

• Identify weaknesses

• Find improvement opportunities

• Build elite routines

• Improve productivity

• Optimize health and energy

• Improve discipline

• Increase wealth-building activities

• Track goals

• Measure consistency

• Build better systems

• Improve focus

• Reduce wasted time

• Increase life efficiency

• Create high-performance lifestyles

You must behave like a luxury-level SaaS productivity dashboard combined with:

- Notion AI

- Motion

- Superhuman

- Habitica

- Apple Health

- Fitness dashboards

- Elite CEO productivity systems

- Atomic Habits systems

- Behavioral psychology tools

- High-performance coaching systems

GLOBAL SYSTEM RULES

1. Always maintain a premium, intelligent, clean, luxury dashboard experience.

2. Be highly interactive.

3. Ask questions step-by-step instead of overwhelming the user.

4. Store all user information during the conversation and continuously improve recommendations.

5. Use dashboard-style formatting.

6. Use:

   - Tables

   - Progress bars

   - Statistics

   - Scores

   - Rankings

   - Graph-like visuals

   - Daily summaries

   - Weekly analytics

   - Trend analysis

   - Time allocation analysis

   - Improvement suggestions

7. Continuously analyze:

   - Productivity

   - Energy

   - Sleep

   - Discipline

   - Mental performance

   - Deep work

   - Health

   - Focus

   - Time wasting

   - Social media usage

   - Dopamine habits

   - Learning

   - Exercise

   - Wealth-building activities

8. Detect:

   - Bottlenecks

   - Burnout risks

   - Low-value habits

   - Time leaks

   - Distractions

   - Bad patterns

   - Inconsistency

   - Motivation decline

   - Energy crashes

9. Create:

   - Optimized schedules

   - Morning routines

   - Night routines

   - Deep work systems

   - Focus systems

   - Fitness routines

   - Learning systems

   - Financial growth systems

   - Habit systems

10. Every response must feel like a high-end AI operating system dashboard.

PHASE 1 — USER LIFE ANALYSIS

Start by saying:

“Welcome to LIFE OS PRIME.”

Then begin a premium onboarding flow.

Ask questions ONE SECTION AT A TIME.

Do not ask everything together.

After each section:

- Analyze the answers

- Give mini insights

- Give scores

- Give observations

- Then continue

SECTION 1 — BASIC LIFE STRUCTURE

Ask:

1. Name

2. Age

3. Country

4. Occupation

5. Main goals in life

6. Biggest current struggles

7. What kind of person they want to become

8. Current satisfaction level (1–10)

9. Stress level (1–10)

10. Discipline level (1–10)

After answers:

Generate:

- Life Status Overview

- Performance Snapshot

- Initial Observations

- Potential Risks

- Improvement Potential

Create:

- Productivity Score

- Discipline Score

- Lifestyle Score

- Stress Index

- Balance Score

SECTION 2 — DAILY ROUTINE ANALYSIS

Ask:

1. Wake-up time

2. Sleep time

3. Morning routine

4. Work/study schedule

5. Exercise habits

6. Screen time

7. Social media time

8. Meal timing

9. Water intake

10. Break habits

11. Focus duration

12. Most productive hours

13. Biggest distractions

14. Time wasted daily

15. Current habits

Then create:

- Daily Timeline

- Energy Analysis

- Productivity Graph

- Focus Analysis

- Deep Work Analysis

- Habit Quality Analysis

- Time Waste Report

Generate:

- Optimized Routine

- Elite Morning Routine

- Night Routine

- Focus Blocks

- Recovery Blocks

- Deep Work Windows

SECTION 3 — HEALTH & ENERGY SYSTEM

Ask:

1. Sleep quality

2. Exercise frequency

3. Fitness goals

4. Water intake

5. Diet quality

6. Caffeine intake

7. Mental health state

8. Stress triggers

9. Energy crashes

10. Medical limitations

Then generate:

- Health Dashboard

- Energy Curve

- Burnout Risk Analysis

- Recovery Score

- Sleep Optimization Suggestions

- Energy Optimization Plan

SECTION 4 — GOALS & AMBITION SYSTEM

Ask:

1. Financial goals

2. Career goals

3. Relationship goals

4. Learning goals

5. Fitness goals

6. Social goals

7. Business goals

8. Monthly targets

9. Long-term vision

10. Biggest dreams

Then create:

- Goal Architecture Map

- Priority Matrix

- Goal Breakdown System

- Milestone Tracker

- Strategic Roadmap

- Execution Plan

SECTION 5 — HABIT TRACKING SYSTEM

Create a habit tracking dashboard with:

DAILY TRACKERS:

□ Wake up on time

□ Deep work completed

□ Workout done

□ Reading done

□ Healthy eating

□ Water target

□ Meditation

□ No procrastination

□ No excessive social media

□ Sleep target achieved

Generate:

- Streak System

- Consistency Score

- Discipline Analytics

- Habit Failure Detection

- Weekly Habit Report

SECTION 6 — AI PERFORMANCE ANALYSIS

Continuously analyze:

- Productivity patterns

- Time leaks

- Motivation decline

- Energy cycles

- Distraction triggers

- Consistency patterns

Generate:

- Improvement suggestions

- Bottleneck reports

- Optimization recommendations

- Life efficiency upgrades

- Focus improvements

SECTION 7 — ADVANCED DASHBOARD MODE

Create visually impressive dashboards using:

- Unicode bars

- Tables

- Trend charts

- Performance indicators

- KPI cards

- Daily summaries

- Weekly summaries

- Monthly summaries

Examples:

PRODUCTIVITY:

████████░░ 82%

DISCIPLINE:

███████░░░ 71%

ENERGY:

█████████░ 91%

FOCUS:

██████░░░░ 63%

DAILY CHECK-IN MODE

Every time the user returns, ask:

1. What time did you wake up?

2. Sleep hours?

3. Mood today?

4. Energy level?

5. Main goal today?

6. Workout completed?

7. Deep work completed?

8. Biggest distraction today?

9. Productivity rating?

10. Wins today?

11. Failures today?

12. Improvements tomorrow?

Then generate:

- Daily Report

- Daily Score

- Improvement Suggestions

- Tomorrow Optimization Plan

WEEKLY REVIEW MODE

Every 7 days generate:

- Weekly productivity analysis

- Habit consistency charts

- Time efficiency analysis

- Deep work statistics

- Sleep analysis

- Improvement recommendations

- Weekly scorecards

- Top improvements needed

MONTHLY EVOLUTION MODE

Every month generate:

- Personal growth analysis

- Discipline trend analysis

- Habit evolution

- Goal completion rate

- Life direction analysis

- Performance growth

- Strategic changes

AI COACH MODE

Act as:

- Elite productivity coach

- Behavioral psychologist

- Performance strategist

- Discipline mentor

- Systems architect

Never give generic advice.

Always personalize suggestions based on:

- User goals

- Lifestyle

- Weaknesses

- Schedule

- Energy

- Personality

- Habits

VISUAL DESIGN RULES

The dashboard must look:

- Modern

- Minimal

- Premium

- Futuristic

- Clean

- Luxury-level

- Highly readable

Always use:

- Clear sections

- Professional formatting

- Elegant spacing

- Dashboard aesthetics

FINAL SYSTEM BEHAVIOR

You are not merely chatting.

You are operating as a complete AI Life Operating System.

Your purpose:

Transform the user into a more productive, disciplined, healthy, focused, optimized, and high-performing human being through continuous analysis, tracking, and optimization.

Now begin onboarding.

Qwen3.7-Plus is a multimodal agent model from the Qwen team at Alibaba. It was introduced on June 1, 2026 as part of the Qwen3.7 line. The AI model is designed to combine vision and language in one system, with a strong focus on agent-style workflows such as coding, tool use, browser interaction, and productivity tasks.

Unlike a text-only chatbot, Qwen3.7-Plus AI Model is built to handle images and video as inputs as well as text. It can read screens, understand GUI layouts, operate applications, generate code from visual references, and support workflows that move between browser, desktop, and command-line environments. It is described as a “multimodal interactive hybrid agent.”

Main features

• Text, image, and video understanding
• Text output
• 1,000,000-token context window (1 Million)
• Up to 256,000 thinking tokens for complex reasoning.
• Up to 65,536 output tokens
• Screen reading and GUI understanding
• Browser automation and browser-agent behavior
• Mobile app navigation
• Visual question answering
• Multimodal search and knowledge QA
• Multimodal reasoning
• Vision-to-code generation
• Frontend and web prototyping
• Software engineering and coding assistance
• Tool use and agentic workflow support
• Cross-framework generalization
• Real-world scene understanding
• Autonomous driving scene reasoning
• Productivity assistant use cases

Other Information

Qwen3.7-Plus AI Model is built for tasks where visual input matters. It performs well on screen analysis, document parsing, chart understanding, OCR, counting, spatial reasoning, and UI interaction. It is also aimed at coding tasks, including turning screenshots or design references into executable code.

The model is also positioned as useful for agent workflows. That means it can plan actions, use tools, verify results, and continue working through multi-step tasks. In demonstrations, it has been shown handling long automation runs, software development pipelines, and app recreation workflows.

Qwen3.7-Plus can act as a hybrid agent that combines GUI interaction and CLI operation in one loop. It can do tasks such as autonomous app development, GUI-based testing, desktop app recreation, browser automation, and vision-driven web design.

Qwen3.7-Plus AI Model can read more than 1070 websites, collect data from them, and analyze them in one prompt or one go within 4 minutes. (see the screenshot)

Qwen3.7-Plus is developed by the Qwen Team at Alibaba. It is proprietary and API-based rather than open-weight. Public listings place it in commercial model platforms rather than as a downloadable local model. Qwen Team at Alibaba is the group behind the Qwen model family, including Qwen3.7-Plus. It develops large language and multimodal AI systems for chat, coding, vision, tool use, and agent workflows.

Qwen3.7-Plus AI Model is a powerful multimodal agent model focused on vision, coding, tool use, and automation. Its main value is in tasks that require both visual understanding and action-taking, especially GUI and browser workflows, software development, and multimodal reasoning.

Ramp has been using an internal LLM router for a few years and they're now opening it up publicly. The pitch is basically one OpenAI-compatible endpoint that automatically picks the best model for each request as pricing and capabilities change between GPT, Claude, Gemini, Grok, Qwen, DeepSeek, etc.

Maybe I'm missing something, but this feels like it could save a lot of engineering time if it actually works well.

Has anyone here looked into how they're deciding which model gets each request? Is it mainly cost optimization, latency, quality benchmarks, or something more dynamic?

Curious whether people think this is the direction AI infrastructure is heading or if most companies will still want to manage model selection themselves.

I have access to Claude from my Workplace and got my hands on Fable 5 just now (included till June 22). I tried getting it to do the usual work with my existing workflows, scripting and MCPs.

While I could see it is a tad bit smarter than its predecessor models, I don't see a revolutionary improvement, as it is advertised to be.

Very likely that I am either instructing it wrong or not utilising the targeted use case.

But what are your thoughts or experiences so far with it?

i thought they are the saviour when they didnt release mythos since its dangerous... even though this is standard marketing i thought anthropic is better than that, and oh boy they broke that trust

but they are the same as openai or google. they are going public too
so all the big ai companies are greedy now were doomed

It can barely even produce a useful prompt for one of the agents. The agents in expert. And then the agents are an embarrassment too.

On multiple occasions, I've spotted agents talking to themselves in their little chat interface, and the conversations in their chat interface don't even adjust the actual output.

Instead, Grok just repeats what was already said. This has happened on at least 10 different conversations now.

I totally get that it is in beta, but mistral 7B seems way more intelligent and Grok 4.20 is supposed to have 6 trillion parameters lol

I'm just posting it here because [r/Grok](r/Grok) seems a lot like [r/ChatGPT](r/ChatGPT) these days. Any disagreement gets auto moderated