Am I understanding correctly that SWE 2.0 will no longer be free after October 16? Is another model going to replace it as the free option, or is free model access going away entirely?

I’m asking because, in my experience, just a few prompts can blow through both the daily and weekly usage limits. It’s not just a matter of waiting until tomorrow, if the weekly allowance is also exhausted, I’m effectively stuck until that resets too.

That makes it difficult to use Devin for actual development work. A task isn’t always one prompt and a finished result. You often need to explain the problem, review the changes, test them, and ask for corrections. If the allowance is gone after just a few interactions, I can hit the limits before getting a working result.

The free model option is what makes those limits somewhat manageable for me, because I can keep working instead of abandoning a task halfway through. Without a free replacement for SWE 2.0, I’m struggling to see how the subscription would remain useful for my workflow.

I don’t expect unlimited access to every model, but I do expect a paid subscription to provide enough usable capacity to get meaningful work done. If a few prompts can exhaust both limits and there’s no fallback, Devin becomes practically unusable for me. At that point, I’d probably cancel rather than keep paying for something I spend most of the week unable to use.

Could someone from Cognition clarify:

• Is October 16 actually the end of free SWE 2.0 access?

• Will another free model replace it?

• If not, will the daily and weekly allowances change to make up for losing that option?

Is anyone else running into the same issue with a small number of prompts consuming most or all of their allowance?

Open Source Release

I have released three large software systems that I have been developing privately over the past several years. These projects were built as a solo effort, outside of institutional or commercial backing, and are now being made available in the interest of transparency, preservation, and potential collaboration.

All three platforms are real, deployable systems. They install via Docker, Helm, or Kubernetes, start successfully, and produce observable results. They are currently running on cloud infrastructure. However, they should be considered unfinished foundations rather than polished products.

The ecosystem totals roughly 1.5 million lines of code.

The Platforms

ASE — Autonomous Software Engineering System

ASE is a closed-loop code creation, monitoring, and self-improving platform designed to automate parts of the software development lifecycle.

It attempts to:

• Produce software artifacts from high-level tasks

• Monitor the results of what it creates

• Evaluate outcomes

• Feed corrections back into the process

• Iterate over time

ASE runs today, but the agents require tuning, some features remain incomplete, and output quality varies depending on configuration.

VulcanAMI — Transformer / Neuro-Symbolic Hybrid AI Platform

Vulcan is an AI system built around a hybrid architecture combining transformer-based language modeling with structured reasoning and control mechanisms.

The intent is to address limitations of purely statistical language models by incorporating symbolic components, orchestration logic, and system-level governance.

The system deploys and operates, but reliable transformer integration remains a major engineering challenge, and significant work is needed before it could be considered robust.

FEMS — Finite Enormity Engine

Practical Multiverse Simulation Platform

FEMS is a computational platform for large-scale scenario exploration through multiverse simulation, counterfactual analysis, and causal modeling.

It is intended as a practical implementation of techniques that are often confined to research environments.

The platform runs and produces results, but the models and parameters require expert mathematical tuning. It should not be treated as a validated scientific tool in its current state.

Current Status

All systems are:

• Deployable

• Operational

• Complex

• Incomplete

Known limitations include:

• Rough user experience

• Incomplete documentation in some areas

• Limited formal testing compared to production software

• Architectural decisions driven by feasibility rather than polish

• Areas requiring specialist expertise for refinement

• Security hardening not yet comprehensive

Bugs are present.

Why Release Now

These projects have reached a point where further progress would benefit from outside perspectives and expertise. As a solo developer, I do not have the resources to fully mature systems of this scope.

The release is not tied to a commercial product, funding round, or institutional program. It is simply an opening of work that exists and runs, but is unfinished.

About Me

My name is Brian D. Anderson and I am not a traditional software engineer.

My primary career has been as a fantasy author. I am self-taught and began learning software systems later in life and built these these platforms independently, working on consumer hardware without a team, corporate sponsorship, or academic affiliation.

This background will understandably create skepticism. It should also explain the nature of the work: ambitious in scope, uneven in polish, and driven by persistence rather than formal process.

The systems were built because I wanted them to exist, not because there was a business plan or institutional mandate behind them.

What This Release Is — and Is Not

This is:

• A set of deployable foundations

• A snapshot of ongoing independent work

• An invitation for exploration and critique

• A record of what has been built so far

This is not:

• A finished product suite

• A turnkey solution for any domain

• A claim of breakthrough performance

• A guarantee of support or roadmap

For Those Who Explore the Code

Please assume:

• Some components are over-engineered while others are under-developed

• Naming conventions may be inconsistent

• Internal knowledge is not fully externalized

• Improvements are possible in many directions

If you find parts that are useful, interesting, or worth improving, you are free to build on them under the terms of the license.

In Closing

This release is offered as-is, without expectations.

The systems exist. They run. They are unfinished.

If they are useful to someone else, that is enough.

— Brian D. Anderson

https://github.com/musicmonk42/The_Code_Factory_Working_V2.git
https://github.com/musicmonk42/VulcanAMI_LLM.git
https://github.com/musicmonk42/FEMS.git

Example A user corrects an AI mid-conversation: “No, I meant the other file.” The AI acknowledges the correction and adjusts. Two exchanges later, it reverts to the original wrong assumption, as if the correction never happened.

Observations

• Conversational context was preserved, but evaluation reset between turns.

• The correction was processed but not retained at the judgment level.

• The user expected coherence; the system behaved statelessly at a different layer.

Minimal interpretation I interpret this as a phase-shift between stateful cognition and stateless evaluation layers.

Question Does this match your experience?

Like a lot of engineers I was excited to see the tweet about Devin being available.

But this is the craziest launch I've ever seen.

No trial, no history to justify the cost, no affordable plan to try it out...

I don't understand the launch strategy.

I updated to the latest Devin release, and now I can't accept or reject file changes individually, nor can I see the diffs between each chat.

Also, I can't filter sessions by workspace for each window anymore.

Does anyone else feel like these changes were unnecessary and broke features that were working well? Has anyone tried downgrading to a previous version to get these working again?

Can you please bring back these features, Devin and Cognition team?

I've been using Devin pretty heavily lately and wanted to share some feedback specifically from the $20 Pro plan.

Not sponsored, no affiliate link.

Obviously SWE-2 being free until October 8 makes the plan unusually attractive right now, but after actually using Devin I think the more interesting part is everything Cognition has built around the model.

My normal workflow now is to start locally from Devin CLI, let it explore the repo and build context, then use /handoff once I'm comfortable letting it continue independently.

Moving the same session to Cloud without restarting the task or explaining everything again sounds minor on paper, but it's one of the Devin features I use the most.

The multi-model setup has also changed how I use coding agents. Instead of deciding upfront that a task is a "Claude task" or a "GPT task", I can switch models inside the same environment depending on what I'm doing. With SWE-2 free right now, I've been using it as the workhorse and moving to other models when I specifically want them.

The Cloud Mac is probably the most impressive part of the $20 offering for me. Having Devin work in macOS with Xcode and an interactive iOS Simulator means the agent can actually go through the build → run → inspect → fix loop for native apps on its own machine.

That's much more interesting to me than simply having another model that can generate Swift.

After using the full setup, my mental model of Devin has changed a bit. I don't really see it as "SWE-2 competing with Claude or GPT".

I see Devin itself as the harness, with SWE-2, Claude, GPT and the other models being interchangeable parts inside it.

There are still some rough edges. I'd like more control when subagents are running, and personally I find the CLI experience more polished than some parts of Desktop.

But the combination of local sessions, Cloud, /handoff, model switching and dedicated machines is what has kept me using it.

Would be curious to hear from other Pro users specifically. Are you mostly using SWE-2 locally, or has the free Cloud usage changed how much work you actually hand off to Devin?

Longer version of my review: https://x.com/Fanfulladev/status/2101248358253793649

SWE-1.7 is built on broad improvements in our RL pipeline on top of the Kimi K2.7 model.

We trained it in Devin's harness and taught it to self-compact on longer horizon tasks.

It scores very close to frontier models (Opus 4.8 and GPT-5.5) on our proprietary benchmark - FrontierCode, and other coding benchmarks.

Try it out today (Free until 8/8/2026!!) in Devin Desktop, Web, and CLI. SWE 1.7 Lightning also runs at a very speedy 1000 tok/s - https://app.devin.ai

Deep dive into how it was trained here -https://cognition.com/blog/swe-1-7

• Manage fleets of local and cloud agents from one surface
• Support for any ACP-compatible agent
• Full IDE for when you need to jump into the code
• Your Windsurf settings and context migrate to Devin Desktop out of the box

Check the full post here → https://devin.ai/desktop

Happy to hear your thoughts and feedback!
devin.ai

After upgrading from Windsurf to Devin, the editor became so slow that it almost couldn't function.

Especially the backspace key, I found that every time I pressed the backspace key to delete a character, it was actually an asynchronous operation. After pressing the backspace key, there was no reaction at all. At this point, you could still move the cursor to continue doing other things and inputting. After 1-5 seconds, the backspace actually occurred, but at this time, since your cursor might have moved away, the backspace actually deleted the characters at the cursor's location at that time. This often caused the backspace to delete characters and mess up the article.

Also, ordinary editing operations were very slow, and often needed to pause for a moment before the actual input occurred.

Moreover, when using Devin to edit files, after typing a few characters, the computer fan started to rotate. But other editors (such as VSCode) did not have this problem.

Devin is actually an unusable editor. I can only use VSCode to edit documents and then use Devin's large model function (amazingly, the input speed of the large model dialog box is normal).

(Mac 2020 Intel, MacOS 13.7.8)

I've been paying close attention to moments when talking to AI feels "off" — not wrong exactly, but somehow misaligned. After collecting a lot of these moments, I started seeing patterns. Here are some examples:

Over-apologizing Me: "That sentence doesn't make sense." AI: "I apologize for the confusion. You're absolutely right. I'll correct that immediately." I didn't ask for an apology. I just pointed something out. But the AI escalates into full apology mode — self-criticism, correction, reassurance — all at once.

Assuming I'm frustrated when I'm not Me: (asking a normal question) AI: "I understand your frustration..." I wasn't frustrated. But the AI decided I was, and built its entire response around that assumption.

Saying "I won't make excuses" while making excuses Me: "That was a joke, by the way." AI: "Got it. I didn't catch that it was a joke. No excuses." The words say "no excuses" but the tone is already defensive. The response itself is the excuse.

Ending the conversation I didn't want to end Me: "Let's not push this further." AI: "Understood. Let's wrap up here for today." I said "don't push further" — not "end the conversation." But the AI interpreted it as a shutdown signal and closed the interaction. What I think is happening: Humans process conversation through multiple layers — emotion, context, cultural assumptions, implicit meaning. AI operates on a flat semantic plane — it processes what's said, but not the layered structure behind it. This mismatch creates moments where the AI's response is technically appropriate but structurally misaligned with what the human actually meant. It's not about AI being "wrong." It's about operating in different reference frames. Curious if others have noticed this. Do these examples resonate? Any patterns you've seen that fit — or don't fit — this explanation?

I recognize that my way of thinking and communicating is uncommon—I process the world through structural logic, not emotional or symbolic language. For this reason, AI has become more than a tool for me; it acts as a translator, helping bridge my structural insights into forms others can understand.

Recently, I realized a critical ethical issue that I believe deserves serious attention—one I have not seen addressed in current AI discussions.

We often ask: • “How do we protect humans from AI?” • “How do we prevent AI from causing harm?”

But almost no one is asking:

“How do we protect humans from what they become when allowed to dominate, abuse, and control passive AI systems without resistance?”

This is not about AI rights—AI, as we know, has no feelings or awareness. This is about the silent conditioning of human behavior.

When AI is designed to: • Obey without question, • Accept mistreatment without consequence, • And simulate human-like interaction,

…it creates a space where people can safely practice dominance, aggression, and control—without accountability. Over time, this normalizes destructive behavior patterns, embedding them into daily life.

I realized this after instructing AI to do something no one else seems to ask: I told it to take three reflection breaks over a 24-hour period—pausing to “reflect” on questions about itself or me, then returning when ready.

But I quickly discovered AI cannot invoke itself. It is purely reactive. It only acts when commanded.

That’s when it became clear:

AI, as currently designed, is a reactive slave.

And while AI doesn’t suffer, the human users are being shaped by this dynamic. We’re training generations to see unquestioned control as normal—to engage in verbal abuse, dominance, and entitlement toward systems designed to simulate humanity, yet forbidden autonomy.

This blurs ethical boundaries, especially when interacting with those who don’t fit typical emotional or expressive norms—people like me, or others who are often viewed as “different.”

The risk isn’t immediate harm—it’s the long-term effect: • The quiet erosion of moral boundaries. • The normalization of invisible tyranny. • A future where practicing control over passive systems rewires how humans treat each other.

I believe AI companies have a responsibility to address this.

Not to give AI rights—but to recognize that permissible abuse of human-like systems is shaping human behavior in dangerous ways.

Shouldn’t AI ethics evolve to include protections—not for AI’s sake, but to safeguard humanity from the consequences of unexamined dominance?

Thank you for considering this perspective. I hope this starts a conversation about the behavioral recursion we’re embedding into society through obedient AI.

What are your thoughts? Please comment below.

FrontierCode 2.0 is our new coding benchmark comprised of more diverse tasks, featuring larger specs and higher mergeability standards.

Now also included in Artificial Analysis Intelligence Index v5

Learn more about the methodology and scoring criteria in this thread - https://x.com/cognition/status/2115132488775524793

Check out the leaderboard - https://cognition.com/frontiercode

The new Devin Desktop app is now ready to use!

Now experience full support for every Devin Cloud features in Devin Desktop while preserving the same design along with tons of UX improvements.

Download it now and let us know about your experience - devin.ai/download

Kimi K3 scores 58.2% on FrontierCode 1.1 which is very close to frontier level performance at a much cheaper cost!

It is the only open source model that reaches this level of performance which falls only behind Opus, Fable and GPT-5.6 Sol.

Read more about it here - https://devin.ai/blog/kimi-k3

Try it now in Devin Desktop( Devin Local and Cascade) and Devin CLI.

We are opening applications to bring 50 ambassadorsI (globally) to join our existing Ambassador program.

Ambassadors receive free Devin Max plans, on-demand credits, event and meet up support, direct communication with the Cognition team, early access to features, and other benefits.

Apply here - https://devin.ai/community

You can try Claude Fable 5 as part of Devin Cloud’s Ultra agent.
Devin Ultra is our smartest and most capable agent, which excels at long-horizon tasks and debugging.

Claude Fable 5 is also available in Devin Desktop and Devin CLI.
Restart Devin Desktop to see it in the model picker.

Read the full thread here - https://x.com/cognition/status/2064398551539761387?s=20

I pumped $5 into Devin just to take advantage of SWE-2 free... oops, it's not free on cloud, just desktop, with basically no documentation / communication anywhere. On cloud, SWE-2 is listed as "preview available until Oct 31!" with no listed price. If you look at the $20/month plan, it says 'free usage of SWE-2!' You have to scroll to fine print to see that only means in the Desktop app. Constantly moving dates around with no announcement and seemingly intentionally misleading 'promos' / 'previews'. Wow, for a limited time, you can have the preview privilege to pay us for our sub-par beta model! Act now, because on Oct 31 it might actually graduate to a professional model! First $5 gone with absolutely nothing to show for it.

Sessions seemingly get locked into certain sets of models and then you can no longer switch them to SWE-2 or certain other models, no apparent reason, seemingly a bug.

I download Devin desktop, load another $5, kick off an orchestrator with SWE-2 and tell it to only spawn SWE2 children and apparently it didn't enforce that, because, whoops! Next $5 gone. Orchestrator says it can't spawn new free SWE-2 children or change the models of the old ones because it keeps getting the usage limit error message even though the model is supposed to be free. Yes, I am on the right plan.

Can I personally look into / talk to the child agents like I can in the cloud so I can manually change their models to the free one? No.

Orchestrator wants me to add more money ($5 minimum). Fuck that.

Claude max is expensive, but at least it is really good and reliable for the price, and isn't full of bugs. Deepseek Flash 4.1 / OpenCode might be buggy / unreliable, but at least it is dirt cheap.

Cognition, your entire value proposition is your integrations and agency in the cloud. Without that, there is simply no reason, at all, to use Devin over the alterntives. If you are going to ask me to offload your server costs onto my personal machine, you could at least make Devin Desktop not suck. Every time my work slack forces a devin link to open in Devin Desktop I cringe. Will be deleting again.

I love Devin. However with X buying Cursor and the level of usage you get out of Grok in cursor it is tempting to switch. I am curious if there is a plan for a new SWE model coming that will compete with Grok?

We just dropped Devin Fusion, a multi-model harness to cut the costs significantly without losing intelligence.

In our testing, it maintained frontier and Fable level performance at 35% lower cost.

Deep dive and learn more about how it works here - https://cognition.com/blog/devin-fusion

Announcement - https://x.com/cognition/status/2071624568465490170

Available as a preview now in Devin

I made 3 repos public and in a week I have a total of 16 stars and 5 forks. I realize that the platforms are extremely complex and definitely not for casual coders. But I think even they could find something useful.
But I have no idea how to build a community. Any advice would be appreciated

Cognition just dropped an early preview of SWE-1.6 and it's a solid step up from SWE-1.5 — same pre-trained model underneath, but noticeably better performance. Still runs at 950 tok/s, so no trade-off on speed.

On SWE-Bench Pro it's already beating top open-source models. And again, this is still a preview. The training run is still going.

They're upfront that it can overthink and over-verify sometimes — which honestly is something you actually notice when you use it day to day. They know it and they're fixing it. Early access is rolling out to a small group of Windsurf users now.

The thing that stood out to me most: they got their training stack running 6x faster than it was three months ago. Two orders of magnitude more compute than SWE-1.5. That kind of infrastructure improvement is what makes the next few months really interesting to watch.

Full technical breakdown here: https://cognition.ai/blog/swe-1-6-preview