Morph (@morphllm) header
Morph (@morphllm) profile picture

inference built for agents

San Francisco, CA
Joined May 2025
Photo by Morph (@morphllm)Photo by Morph (@morphllm)Photo by Morph (@morphllm)Photo by Morph (@morphllm)Photo by Morph (@morphllm)Photo by Morph (@morphllm)Photo by Morph (@morphllm)Photo by Morph (@morphllm)Photo by Morph (@morphllm)
Morph retweeted
DeepSeek V4.1 Flash Abliterated from Morph is now live on Opper AI. @morphllm launched it last week for defenders, so they can run the kind of model attackers already use against their own infrastructure, for red teaming, vulnerability research and agent evaluations. Access stays gated like on Morph: sign up, tell us your use case and we enable it on your account. opper.ai/morph/deepseek-v4-1…
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We're excited to announce a new partnership with the team at @morphllm to bring the latest abliterated open models to MindFort Swarms. In response to the rapid increase of agent-led cyberattacks, we needed a partner that was able to meet the inference requirements needed to run large scale agent swarms. MindFort is designed to let teams safely deploy security swarms to quickly find vulnerabilities in their products and services. Try today: mindfort.ai/morph
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the one-man inference provider i hit a $6m run rate as a solo dev morph was 1 person until last week the first inference provider to be live with kimi k3 without early weights access 250 customer slack connect channels i could've made morph a 1-person $1b company, but it would've been for the Ego instead of whats optimal we are no longer a 1-person company. but we will be the first 10-person $10b company. everyone joining has had multiple offers from frontier labs if you’re top 0.0001% at having adhd, dm me
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fixed it
im no longer excited about open releases like what is this bullshit where is my $5 Kimi-K3 endpoint
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Replying to @morphllm
@morphllm has signed the open weights letter the models train on the inheritance of humanity. humanity should remain capable of possessing, understanding, and extending them. the deepest argument for open weights is not that open models are cheaper, or even that they create more competition. it is that models are compressed civilization. they train on the accumulated work of scientists, programmers, writers, artists, institutions, open-source communities, and generations of people who made their knowledge legible to others. model developers add enormous amounts of engineering, compute, judgment, and risk to this inheritance. that work is difficult and deserves to be rewarded. but it would be a strange equilibrium if models could learn from the open output of an entire civilization while the resulting intelligence remained available only through a small number of closed interfaces. open weights turn intelligence from a service you receive into a material you can work with. you can inspect it, modify it, specialize it, run it on your own infrastructure, and preserve what you build on top of it. a hospital, factory, university, startup, or country can accumulate capability without depending on the permission of a single provider. this is how real technological ecosystems form. not around one perfect oracle, but around thousands of people adapting a shared foundation to problems its original creators could never have anticipated. there are real risks to releasing powerful models, and openness is not automatically safe. but concentration is not automatically safe either. a future built around a handful of closed systems is fragile in ways we are only beginning to understand.
For my first post, I’m sharing a letter @NVIDIA signed on why open models matter. AI will transform every industry, power every company, and be built by every country. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. The world needs both frontier closed models and frontier open models. images.nvidia.com/pdf/Open-W…
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morph is now a top 30 provider on @OpenRouter our competition has raised over 100x what we have
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we also built it to fall back to cheaper models when a task doesn't need the expensive ones, thanks to @morphllm routing. keeps it fast and affordable without losing quality.
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Excited to be working with Merge to bring GLM 5.2, deepseek v4 flash and more to their gateway!
Morph (@morphllm) just landed as a model provider on Merge Gateway. It brings its own models such as Morph Compactor along with other open sourced models like Deepseek V4 Flash. Like most providers on Gateway, its calls run under zero data retention. Add your own Morph API key or route on Merge Gateway's.
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excited to be speaking at @aiDotEngineer on July 1st about how we use autoresearch to discover kernels for weird hardware. we're making models faster in environments that didnt make sense to buy until now come listen
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another week another record
unfortunately, due to the gpu shortage, we’ve had to lay off some of our silicon and increase human headcount to 3 despite this setback, we continue to scale
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the general applied standard intelligence compute company
morph is 2 people we spend 10x more on gpus than salary we’re hiring for the first sub-10-person billion-dollar company. join us
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Join us in welcoming @morphllm Founder, @tejasybhakta to the AIE Miami lineup! Don't miss his talk 'Everything is Models' next week on the big stage! Get your tickets: ai.engineer/miami
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warpgrep_github_search from @morphllm is probably the most unfathomoly unfair advantage you can have right now. 10x better than grep app Even beats Ctx7 tbh docs.morphllm.com/sdk/compon…
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Morph retweeted
Agents don’t need bigger models. They need better tools. Morph trains coding subagents. Not for humans. For frontier models. Fast Apply edits at 10,000 tokens/sec. WarpGrep handles code and log search. Both keep the main model’s context clean Because when context gets too large, performance drops. Now Morph is pushing coding subagents even faster One newer model runs at 33,000 tokens/sec: docs.morphllm.com/sdk/compon… 🎙️ @tejasybhakta, Founder & CEO, @morphllm on @fondocom @thestartpod w/ @davj
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Perfect compaction is a prerequisite for long-running agents. It’s the difference between a country of geniuses and a pile of clankers. #unLobotomizeClaude
Introducing FlashCompact - the first specialized model for context compaction 33k tokens/sec 200k → 50k in ~1.5s Fast, high quality compaction
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Compaction should feel invisible It should be fast, accurate, and cheap some of our beta users were confused because they didn't notice compaction happening in their coding agent now mission accomplished
Introducing FlashCompact - the first specialized model for context compaction 33k tokens/sec 200k → 50k in ~1.5s Fast, high quality compaction
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static testing is useless in 2026 Our log inspector had a bug in the PR with the bugfix, Glance realized its account didnt have logs to test. it organically went to the playground and used it to generate logs -> properly tested the PR
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Really cool that claude code can now dynamically resize chrome windows. Glance can too now
Introducing Glance, a fast browser agent trained with RL to test code changes. We're releasing this with BrowserBot, which puts a video of it testing your app right in your GitHub PRs Frontier models are trained to be helpful. Our model is trained to be bad at using software.
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This might seem niche but if you've been tinkering with firing off multiple coding agents, you'll get this and why it's cool. Right now if you are building in the terminal / with any claude code/codex/droid type harness there isn't much visibility about what's actually happening. It just does things, which is great most of the time. But as things get more serious more visibility over PRs is probs wise. Hadn't actually found anything that did it but looks like these guys just made it.
watch all of your PRs
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monitor the situation watch your org's PRs
Introducing Glance, a fast browser agent trained with RL to test code changes. We're releasing this with BrowserBot, which puts a video of it testing your app right in your GitHub PRs Frontier models are trained to be helpful. Our model is trained to be bad at using software.
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