We're growing our GTM team across commercial verticals, public sector, and partner sales. We started with the hardest customers in the market and now we're taking that everywhere. Join us if you like to work hard, build, and win. 8090.ai/careers
Replying to @USAFacts
@USAFacts has been doing great work for a while. I wonder how much more productive our conversations on the challenges and opportunities in our country would be if we had much more of this data-driven format in our conversations… youtu.be/A7_1Mqhmf4Y?is=glT7…
We love working alongside engineering teams at companies like @ifs to tackle really hard problems on short timelines together. Bring us something we can partner on at 8090.ai.
This week, we went to @IFS Unleashed to announce IFS Intelligence.
Zach, our eng lead on the project, chatted with Harshana from IFS about building it as one team across two companies.
More to come.
Max Tynan retweeted
liz warren: we’re gonna stop the mayonnaise company from buying a hot sauce company
elon musk:
These are the best workout shoes around and it’s not even close. 🤝@KingJames
Max Tynan retweeted
Join us for a live session on modernizing COBOL and Assembly systems with Aditya Bhatnagar & Zachary Gonzalez from our technical staff.
We'll cover how legacy modernization works across the public and commercial sectors, then walk through our business rules work on CMS's ClaimsCore program as a case study.
October 7 · 9 am PT / noon ET
Registration below.
Max Tynan retweeted
One founding engineer at Utopia Studio built Mentix, a clinical platform for surgical mentoring, in 3 months.
Mentix began with Dr. Amyn Haji, who trains surgeons in advanced endoscopy at King’s College Hospital. He worked with the team to write the product Requirements. Software Factory turned them into Blueprints and Work Orders. A working prototype was ready in 3 weeks.
"When the people who hold the knowledge help write the requirements, and our stack goes in with them, the work order knows what matters and why." James Cannan, founding engineer at Utopia Studio
Mentix TV can broadcast a live procedure to a room of trainees. Live sessions were running by July, including a masterclass at King’s.
Love this customer story. Thanks @matthew_kruer and the @BISSELLclean team
Melville Bissell patented a carpet sweeper in 1876. In 1889 his wife Anna took over and became America's first woman CEO, building it into the largest company of its kind in the world and setting its early standards for trademarks and patents.
BISSELL is still family-owned 150 years later.
Every product is built from parts, and every part needs an approved name before engineering can start. That approval ran on Excel forms, applied by hand against rules that lived in the heads of people who have been there 40 and 50 years.
We built the system that runs it now. 7,000+ parts submitted, 81.4% approved in seconds, and reviewers spend their time on the ones that need a decision.
The full story: 8090.ai/customer-stories/bis…
BISSELL is one of our earliest 8090 customers. We've learned so much about manufacturing, how leadership teams underwrite investments in technology/AI, and about how much opportunity there is in streamlining 'unsexy' workflows. The company is celebrating their 150th this year!
An engineer sends twenty part names for approval and waits on a reviewer's calendar, even for the sixteen a computer could have approved on sight.
BISSELL retired that process. 8090 wrote the reviewers' rules into software, and 81.4% of parts now approve in seconds at half the annual cost.
The full story: 8090.ai/customer-stories/bis…
This was some of the most complex, rewarding work we've done at 8090. We are so proud to have had an impact for the US taxpayer and @CMSGov. 🇺🇸🚀
Here is the first case study of what 8090 can do.
Working with CMS, our Enterprise team decoded a 50yr, 18M repo of COBOL and Assembly that governs billions in healthcare payments. We documented 100,000+ business rules in plain English using Software Factory, each traced to its exact source line.
The people who own Medicare policy can now read how their systems behave and change as they see fit.
Full story in the thread below.
We can do this for any large enterprise who have big, legacy codebases.
I feel incredibly fortunate to be a part of such a high-agency, smart team, and to work with so many interesting customers on challenging problems & opportunities. Some learnings from a year and a half in at 8090...
1\ There is an immense amount of technical debt in the enterprise and Gov't. These are legacy systems, written in COBOL, Assembly, etc. Everyone knows this, but folks are just starting to come around to the fact that Agents/frontier models can't effectively reverse-engineer and accurately capture requirements for those systems, which means transformation/modernization programs often stall out in the early innings. We've built unique ways to extract accurate business rules at high speed (days/weeks) across many systems. This builds early confidence in the fun part: forward-engineering the new systems, because we're not missing anything the old systems need to do, and dragging timelines out trying to get to ground truth. Ford having to bring back hundreds of engineers with legacy system knowledge is a proof point here...
2\ Time and Materials (T&M) is a fundamentally broken model for legacy modernization. We do our modernization work on a deliverables basis, and deliver outcomes. People want system maps, rules extraction, future architectures and engineering plans, tests & certifications for new systems. They don't want hourly bills for missed milestones and powerpoint slides.
3\ Enterprises have done organizational gymnastics around COTS for too long. They add people, process, and layers to compensate for the functionality gaps from the software they've bought over the years. They want a roadmap of one and are ready to bet on their domain expertise and ideas for how to grow & improve their businesses. We free our customers of the unsexy but important jobs to be done of capturing/writing detailed requirements, building engineering plans, and building/hosting custom software for them. Levie is spot on when he calls out that the idiosyncrasies of business workflows are the hardest part of building custom software. We've made good progress at cracking that (with much more to go).
4\ No one wants to give away their domain expertise to closed-source model providers, and no C-level executives believe the agreements they have with frontier labs will truly protect their knowledge from leaking to these firms and ultimately making its way into models for others to benefit from. The way we build at 8090 keeps IP safe.
5\ We are not cheap (our Custom Software and Modernization work all starts at 7 figures/yr), but customers are willing to make meaningful investments in replacing the key software that powers their business when you show up with a different model, iterate quickly and with quality, listen intently to their needs, and are generally fun to work with.
6\ We are so early. It is very much still Day 1.
Really fortunate to spend time at the White House yesterday with @sinasojoodi. So damn impressed by the people in our Gov't quietly doing the hard, complex, "I was told this would be impossible", work to improve the agencies they're a part of.... 🇺🇸📈🚀
Max Tynan retweeted
Judging by my tl there is a growing gap in understanding of AI capability.
The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.
But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along.
So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions.
TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
Max Tynan retweeted
Today I’m very excited to announce a global partnership between 8090 and EY.
EY will adopt 8090’s Software Factory and use it to help their customers break free from slow, costly and failure-prone legacy enterprise software using our AI-native software factory that reimagines the software development lifecycle.
EY is a massive global organization with more than 400,000 employees and tens of thousands of customers in every sector of the global economy.
8090’s Software Factory is the new way organizations can move to a focus on building software that is powerfully bespoke, hi quality, easy to maintain, easy to migrate and always consistent and up to date. No drift, no cruft, no waste.
Companies that build with Software Factory grow faster, are more profitable and are more adaptable in moments of change like we are witnessing today.
Let’s rewrite all the enterprise software in the world. EY and 8090 will work together to do its part.
Max Tynan retweeted
Software Factory Stories - Episode 3. Meet @jonathankopnick , CTO at @meet_tie. Here's his story.
Max Tynan retweeted
🚨BREAKING: Alibaba tested AI coding agents on 100 real codebases, spanning 233 days each.
the agents failed spectacularly.
turns out passing tests once is easy. maintaining code for 8 months without breaking everything is where AI collapses.
SWE-CI is the first benchmark that measures long-term code maintenance instead of one-shot bug fixes.
each task tracks 71 consecutive commits of real evolution.
75% of AI models break previously working code during maintenance.
only Claude Opus 4 stays above 50% zero-regression rate. every other model accumulates technical debt that compounds over iterations.
here's the brutal part:
- HumanEval and SWE-bench measure "does it work right now"
- SWE-CI measures "does it still work after 6 months of changes"
agents optimized for snapshot testing write brittle code that passes tests today but becomes unmaintainable tomorrow.
Alibaba built EvoScore to weight later iterations heavier than early ones. agents that sacrifice code quality for quick wins get punished when consequences compound.
the AI coding narrative just got more honest: most models can write code. almost none can maintain it.
















