Spring 2026

Reason Machines

Reason Machines builds Reason Agent, the autonomous software engineer…

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What they do

Autonomous engineer that reproduces bugs and ships tested pull requests

How close to us

4, very close

Writes code, tests it and ships review-ready pull requests; no memory layer, buyer size not stated.

Wedges

  • W1 Trusted 24/7 coding for small startup teams direct
L1 Company brain (knows the organisation) 1, light
L2 Agent team (runs agents, tools, approvals) 2, clear
L3 Does and learns (finishes, checks, measures, updates the brain) 2, clear

Who buys

Firm size
not known
Buyer type
developer
Role
head of engineering, and individual developers who adopt it themselves
UK
not known
Their own description (from YC)

Reason Machines builds Reason Agent, the autonomous software engineer for engineering teams. Reason connects to GitHub, reproduces bugs, writes code, passes tests, and ships review-ready pull requests with recordings and logs across cloud environments.

Full teardown

Reason Machines (YC Spring 2026): teardown vs XMarts

In one line: Reason Machines sells Reason Agent, a coding agent that takes a task, tests its own work and opens a pull request with proof, from the web, the terminal, a Mac app or Slack [yc] [site] [press]. Threat: 2 because it is the same buyer and problem as W1, but it is two people, four months into this product, with no named customer [yc] [site]. Expect a YC partner to name it anyway: it is in the same batch pool as us, it is live, and it competes head on with Codex, which is one of the engines we run on [blog].

1. Fact file

Item Finding Source
What they say "Reason is the world's best coding agent." Sub-line: "Autonomous Software Engineer." YC line: it "reproduces bugs, writes code, passes tests" and ships review-ready pull requests. [site] [yc]
Founded, team, funding Founded 2026, YC Spring 2026, 2 people, San Francisco, YC partner Aaron Epstein. Funding: a job-board listing says $500K seed, a Dealroom page says $125K; the two disagree. $500K matches the standard YC deal, so I used it in the front matter (inference). [yc] [press]
Founders Adi Singh (CEO): UC Berkeley EECS and NTNU, built a Rust IDE from scratch, applied ML at Scale AI. Sven Myhre (CTO): UC Berkeley EECS, scaled ML infrastructure, won the Norwegian AI Championship 2025. What YC likely saw: two technical founders who had already shipped an IDE and a prior launch (inference). [yc] [press]
Customers None named. The site shows only "NVIDIA Inception Program member" and "Y Combinator". No logos, no quotes. [site]
Price Not public. Product Hunt says it runs "locally on your own machine for free" and lets users bring their own model subscriptions. Cloud price and paid tiers not stated. The only open job is an engineering intern paid in equity, so there is no sales price hint. [press] [site] [jobs]
Path Launched as Ara, "The World's First Self-driving IDE" (YC launch post; Product Hunt history dates an Ara launch 3 June 2026). By July 2026 the cloud coding agent was the product. Reason Agent 1.1 post is dated 10 Oct 2026, and ara.so now redirects to reasonmachines.com. The Product Hunt maker says Reason "builds on the autonomous coding direction we were taking with Ara". [yc] [press] [blog] [site]

Data conflicts to know: Product Hunt says "Launched in 2025" while YC says founded 2026 [press] [yc]. The YC list shows a former name "EasyClaw"; I found no source tying EasyClaw to this company, so that name is UNVERIFIED and left out of the Path.

2. Their proposition, reverse engineered

Part What they do
Who buys Engineering teams: YC says "engineering teams" and lists enterprise software [yc]. The free local mode and "bring your own subs" point to individual developers adopting it first (inference) [press].
The pain Not stated as a pain. The pitch is a promise: a better, cheaper coding agent. "The model sets the pass rate; the harness sets the bill." [blog]
The promise Hand it a task, get a pull request you can review, with proof the change works [press] [yc].
How it works Each session gets its own computer: terminal, live browser, your repo. It reads code, runs tests, spawns sub-agents, opens the PR, fixes CI, handles the merge queue. Sessions keep running when the laptop closes. Runs local or cloud. Imports Claude Code, Codex, OpenCode and Devin sessions. Rolls over to another connected plan when one hits its rate limit. [site] [press] [blog]
The output A pull request with a walkthrough video, before and after screenshots and test results [press] [blog].
Proof style A benchmark and a cost number. They claim 65.1 vs 62.5 for Codex on the "Coding Agent Index", 22% lower cost per solved task, 15 to 23% cheaper per attempt, over 303 tasks, with public job links [blog]. They also claim they built about 2 million lines of their own code with Reason [press].
Who sells it Self-serve sign-in; no sales team visible. One intern job only [site] [jobs].
Price model Not public. Free local, bring your own subscription; cloud pricing unknown [press].
The story The enemy is the wasteful harness around a good model. Their belief: "That is all we focus on" (coding agents, nothing else) [site] [blog].

Check on their numbers: a search of the independent Artificial Analysis index found Codex listed at 67 on a different model setting, not 62.5, and no entry for Reason. Their 65.1 is self-reported and unverified [press] [blog]. Their benchmark name "Terminal Bench 4.0" is also not the name that index uses [press].

3. Positioning

  • Category they claim: "the world's best coding agent"; "Autonomous Software Engineer" [site].
  • Category a YC partner would put them in: coding agent harness, competing with Codex and Claude Code on cost and score (inference from [blog]).
  • What they want to be known for: the cheapest way to get a good result from a coding model.
  • Where they sit on the map: layers L1/L2/L3 = 1/2/2, buyer = size unknown, developer.

4. Three layers vs ours

Layer Them (score + evidence) Us (Marton's logs show)
L1 Company brain 1 (inference). A third-party article says the Ara-era product kept "stored context" for teams [press]. The Reason site and the 1.1 post say nothing on memory or learning [site] [blog]. A vault of about 1,400 pages, 112 call transcripts and about 8,700 customer quotes, searchable by meaning.
L2 Agent orchestration 2. Sub-agents for parallel work, orchestration of other sessions, scheduled recurring work, a human reviews the PR [site] [blog]. About 5,600 jobs run by agents from three makers (Codex 4,756, Claude 839, Gemini 46), each built in its own copy of the code.
L3 Execution and learning 2. Does the work and checks it: tests, screenshots, video, CI fixes [press] [blog]. No claim that checked results change the next run. Each job is checked by a different AI than the one that wrote it, then automatic tests. About 160 failure records, each with an automatic check. Only about 15% of jobs reached the main code, and merges fell from 454 in July to 43 so far in October.

5. Same and different

Topic Same or different Them Us
Core belief Same Agents should run on their own computer and prove their work [site] [press]. Marton's logs show jobs built in their own copy of the code and tested before they can merge.
Several model makers Same Imports sessions from Claude Code, Codex, OpenCode, Devin; rolls over to another plan at a rate limit [press]. Marton's logs show work routed across OpenAI, Anthropic and Google, with a capped account handing the job to a peer.
Kind of work Same Pull requests: bugs, speed-ups, features [site]. Marton's logs show code jobs on his own repos.
Who checks Different The same agent tests its own change, and a human reviews the PR [site] [blog]. Marton's logs show a different AI reviews each job, never the one that wrote it.
How it improves Different By better harness design (small toolset, parallel calls, cache). No claim of learning from accepted or rejected work [blog]. Marton's logs show rules added after each failure; no customer proof of this yet.
Proof Different Benchmark score and cost per task, self-reported [blog]. Counts of jobs, checks and failure records. No benchmark.
Size Different Two people, free to start [yc] [press]. Two founders, one user (Marton).
Buyer Different or unknown A developer or engineering lead picks it up directly (inference) [press]. Target: small startup teams hiring engineers (brief). No customer yet.
Price Unknown Free local; cloud price not public [press]. Not set yet.
Delivery Different A product you log into [site]. A system run by Marton on three Macs; Mac-bound.

6. Wedge overlap

Wedge Fit Why
W1 Trusted 24/7 coding for small startup teams direct Same problem (agents that ship tested code while you do other things) and same buyer (engineering teams) [yc] [site]. Their sessions run when the laptop is closed [press].

Not a fit, but watch: W8 (skill upkeep from accepted and rejected work). Reason does not claim it today [blog]. The nearest rival for the W1 plus W8 angle stays Nessie (brief). W2 (is my agent fleet producing?): Reason's site shows a sample spend dashboard in a demo, which is not a product claim [site].

7. Whitespace (where they stop)

  • Layer gap: the product stops at a checked pull request. Nothing public says an accepted or rejected PR changes how the next run works [blog] [site]. To own it we need a rule-change log tied to checked runs, with rework falling, on one customer's code.
  • Buyer gap: not clear. They say "engineering teams" but show no team features, no logos and no sales role [site] [yc] [jobs]. To own it we need one named small startup team with the pain on file. Do not call this a gap yet.
  • Number gap: they count benchmark score and cost per solved task [blog]. They show no merged-PR rate, no rework rate and no hours saved for a real team. To own it we need one team's number: share of agent PRs merged without rework, before and after. Note our own number is weak today (about 15% of jobs reached main).
  • Price gap: price unknown [press] [site]. Free local use sets a very low floor. We can not win by being cheaper per task; they lead on that claim (they claim).
  • Speed gap: not a gap. They have a free sign-in and a local mode [press] [site]. Our system is Mac-bound and onboarding is unproven.

8. For the YC application

  • Proven today: Marton's logs show his system has run about 5,600 jobs across agents from three makers, each checked by a different AI and then automatic tests, with about 160 failure records that each added an automatic check. Reason's public proof is a benchmark and a cost per task, and the site names no customer [blog] [site].
  • Draft answer to "how are you different from Reason?": DRAFT. "Reason makes one agent cheaper per task. Our system runs agents from three makers, has a different AI check each job, and writes a record and a check for every failure." Founders say it their own way.
  • Future claim (needs proof): "Our agents' work gets merged more often, because every rejection becomes a rule." Needs one startup team's repo run for 4+ weeks, with the share of agent PRs merged without rework before and after.
  • What we understand that they don't: the hard part of 24/7 coding is trusting the output and keeping the line moving, not the cost of a task. proven only as a lived experience: Marton's logs show cost blowouts, silent stops and a 15% merge rate. needs proof that a team feels the same pain.
  • Copyability (M3): medium. They already open PRs with proof, orchestrate sessions and work across several models [site] [press], so a cross-check by a second model is a feature for them (inference). Learning from accepted and rejected PRs needs weeks of real customer data, which they have not shown [blog]. Chasing it fits a harness company less than cutting cost does (inference). Not hard, so M3 can not score above 3 against Reason alone.
  • Why now (M4): None found in this teardown. Their own timeline (a new model generation, a rename in October) is general AI progress, not a dated change that helps us more than them.
  • Do not claim: "more trusted than Reason" (no side by side test). "Our agents ship reliably" (about 15% of jobs reached main). "Cheaper than Reason" (we have no per-task cost number). Anything that calls the market empty: Reason is live, in YC and ranked #8 on its Product Hunt day [press].

M2 (what YC funds now) is NOT judged here. It comes from the themes of many companies in Pass 3.

9. Ideas worth taking

  1. Make the proof part of the output: a short video and before and after screenshots on every PR [press]. A buyer sees the work without reading code.
  2. Publish the benchmark run with public job links, so anyone can check the number [blog]. Use the same shape for our own number once we have one.
  3. One plan hits a limit, work moves to another plan [press]. Marton's logs show we already do this; it is a good one-line demo.

10. Unknowns

  • Who pays, and how much. Look for a pricing page after sign-in, and ask a user.
  • Real users. One search summary mentioned a traction claim from a founder's social post; its source was a LinkedIn page, which I did not open. Treat as UNVERIFIED.
  • Funding: $125K vs $500K. Check the YC deal terms for Spring 2026 or a founder post on X.
  • Whether the Ara-era "stored context" still exists in Reason. Try the product, or read the changelog.
  • Whether the Reason 1.1 numbers hold. Look for an Artificial Analysis entry or the public GitHub runner named in the post.
  • SOC 2 status. The trust page returned only its title, so Type 1 or Type 2 is unknown.
  • Whether they sell in the UK. Nothing found.

Sources

Technical detail
Slug
reason-machines
Lens hash
179096c479
Pass
deep
Stale
no