Ronin

20 / CEO of Arcane / Angel Investor / engineering virality for AI companies

[Ronin @DeRonin_]

https://t.co/b4fTEzmv6K

[Ronin @DeRonin_]

stop renting your business's back office

1 signup.. and you've got a live website, a booking calendar and marketing going out, all on one platform you actually own

Durable just shipped all of it as your AI business builder.. you set it up once, it runs the rest:

[ by the end you'll have ]:

* a website live in minutes, built from nothing but "here's what I do"
* discovery handled, SEO and the Google Business listing, the stuff that actually gets you found
* marketing content and social posts generated for you, no agency, no retainer
* bookings and customer follow-up running on autopilot

[ the workflow ]:

tell Durable what the business is, cleaning, landscaping, detailing, consulting, training, it doesn't matter

AI builds the site, writes the copy, sets up the booking flow, no dev, no designer

it keeps generating marketing assets and posts, so you're not starting from a blank page every week

customer management and back office run in the same place, so nothing lives in six different tools

IDEA: this isn't a starter kit for "anyone with an idea" anymore, it's built for real service businesses that already have clients and just need the operations to catch up

set it up once, and it's the difference between chasing admin and actually running the business

most people are still cobbling this together from five subscriptions and a VA.. you can just run one platform

and they're giving it away free to 100 people right now, instructions are in the post

You have no more excuses

[James Clift @jamesclift]

F*ck jobs. No one likes them anyway.

Durable helps you turn any skill you already have into a real business.

RT + reply "Durable". 100 people get 100% off.

[Ronin @DeRonin_]

@jamesclift that's might be interesting for my solo-agency

[Ronin @DeRonin_]

@dimakhanarin So extraordinary concept

[Dima @dimakhanarin]

Introducing Codos: The first virtual Chief AI Officer.

AI is crushing all benchmarks but real companies still struggle to see P&L impact.

Codos interviews employees, deploys automations across all functions and gets smarter over time while running on your own servers.

Our NASDAQ-listed and PE-backed customers are adding millions to their bottom line months ahead of schedule and we are proud of the first results we deliver.

It’s time to turn the 500BN AI-transformation market into software and unlock the impact for the real economy.

[Ronin @DeRonin_]

@charliejhills pages don't exist

[Ronin @DeRonin_]

@alex_prompter I am just on the way to become ahah

Learn it hardly, even stopped being so active on X becayse of it

[Ronin @DeRonin_]

How to get a job as a Robotics Engineer:

robotics is the one frontier field where the entry level still doesn't ask for a degree

1 in 5 robotics jobs posted right now is a technician role, and most of them only need a certificate or a two year degree

so here's my workflow for getting a job as a robotics engineer:

1: pick one direction and drop the other two

robot learning pays the most and everyone wants it

autonomy and mobile robotics has the most openings by a wide margin

embedded and mechatronics is boring and you'll never be out of work

2: build things that moved, and write down the numbers

recruiters here open your github before they read your CV

what gets you through:

- a commit history where you're visibly fixing things, not one big final push
- real hardware with reliability numbers, sim doesn't count
- datasets on the lerobot hub
- commits to ROS 2, Nav2, MoveIt, Isaac Lab, LeRobot

3: build your hardware in public

the best way to prove your knowledge is the recognition of your skill from masses

just build anything you want and share it on X/IG/YT

good example of the guy who gets offers from Tier-S level robotics companies (you can copy his strategy):

go to IG
type in search: "aykhanium"
and check which rubrics he does to be recognized
repeat.

4: write down what broke

everyone posts the demo that worked

almost nobody writes up the four things that failed first and how they found each one

that's the part you can't fake from a tutorial, and it's the part that survives the third question in an interview

cheat-codes to stand out and get into the top 1%:

1. take the shift nobody wants

teleoperation pays around $28 an hour. figure posted a humanoid robot operator at $25 to $35 with no degree asked for

it's just driving a robot around a lab

but it puts you inside a frontier company with a badge on, and now you're a person they know instead of a CV in a pile

2. sell the integration, not the robot

the arm is about 25% of what a project costs

the other 75% is engineering, safety, and making everything talk to each other

a $35k arm turns into an $80k system, and that $45k is your job

3. go where nobody's competing

66% of robotics projects get delayed by certification

functional safety pays well and almost nobody bothers learning it

4. C++ and Python, both

every Figure and Skild listing I read asks for both, not one

5. delete the ROS 1 from your repos

recruiters call it out by name as a red flag

if there's still a catkin_make sitting in your github, that's the first thing they see

main insight:

$47.4B went into physical AI in the first half of 2026

the BLS still projects 1 to 2% job growth in the occupation

the money showed up years before the headcount will

so the people getting in right now aren't winning interviews, they're walking through the technician door and moving sideways once they're inside

that's like to be hired in OpenAI in 2019...

and one more thing

nothing you learn here goes stale

a PID loop works the same as it did in 1990, and the arm you fix this weekend teaches you something you'll still use in ten years

you can't say that about anything else in AI right now...

[Ronin @DeRonin_]

https://t.co/b4fTEzmv6K

[Ronin @DeRonin_]

nah this is actually insane

pocket fm nearly shut down

it's now at $500M ARR

→ 200M+ listeners
→ 135 minutes a day per US listener
→ tiktok gets 53.8
→ 70% of the money comes from america

here's the actual path:

they pivoted 10 times in 2 years

podcasts. audiobooks. music.

all dead.

the try after that was serialized fiction sold one episode at a time, $0.99 at the cliffhanger

$21M → $500M in under 3 years
$198M → $500M in the last year alone

then they removed the thing that caps every content company

the cost of making the content

elevenlabs partnership
audio production down 90%
across 30,000+ hours

and today they removed the other cap

sherpa, an ai writing partner trained on 100M+ hours of retention, coin spend and drop off data

one line idea in
a full season out

it has no idea what good writing is

it knows the exact second someone stopped listening, and the exact cliffhanger that made them pay

75,000+ series
100,000+ hours of content
$33M already paid out to the people writing it

the lesson isn't "use ai"

everyone pointed ai at their marketing

pocket fm pointed it at the two things actually capping them

how fast they could record
how fast they could write

demand was never their problem

supply was

go find out which one you're short on

[Rohan Nayak @RohanNayak2]

Introducing Sherpa: the most advanced fiction writing AI

We accelerated from $250M in ARR to $500M because Sherpa helped increase content production by 1200% in 1 year

Sherpa was trained on 5.5B hours of playtime with minute by minute dynamic retention data.

550K+ creators have produced 2.6M hours of content annualised using it

Pocket FM is like Netflix for audio-only dramas, with our own pool of one-person studios.

10% of eligible writers on Pocket FM make >$200K
One blockbuster produced >$100M in revenue
3 writers have become millionaires in <2 yrs

We built Sherpa to enable anyone to make >$1M by writing world-class fiction stories:

1. The Idea: Drop a 1-2 sentence concept. Sherpa interrogates it like a veteran editor on tension, stakes, and psychology
2. World & Characters: It builds out the complete lore, tone, and character psychologies
3. Sub-Plot planning: Breaks the premise into arcs, arcs into episodes, and episodes into scenes
4. Scene-by-Scene Generation: Outlines and drafts entire episodes, with you able to steer, rewrite, or override anytime
5. Editorial Review: Stress-tests every draft for pacing, engagement drop-offs, prose, and coherence before it locks
6. One-Tap Production: Pick a voice, convert to audio drama, and publish directly to Pocket FM’s millions of listeners
7. Global Scale & Monetization: Revenue-share on performance, with automatic localization so you earn across international markets

Test Sherpa for free here: https://t.co/R39M5o0Miz
_____________________________________________

Generic LLMs fail at serialized fiction because they lack a long-horizon narrative reward function.

Sherpa solves this through three core technical leaps:

1. Narrative World Model (State Tracking & Retrieval): Context windows degrade over long runs. Sherpa constructs an evolving semantic knowledge graph tracking character states, secrets, and plot dependencies. High-speed retrieval surfaces exact context on demand, maintaining zero continuity decay across hundreds of episodes

2. Hierarchical Story Planner: When writing a 500-episode story like Naruto, you need to plan 100s of sub plots. Rather than generating linearly, Sherpa decomposes narrative across discrete levels: season -> arc -> sequence -> episode -> scene.

Rather than generating everything upfront, like a generic LLM, Sherpa uses progressive planning and dynamic replanning. As the story evolves, it identifies what changed, traces the downstream impact, and replans only the affected parts.

3. Prose Engine (Trained on series' retention data): LLMs write robotically, but serial fiction needs emotion, tension, pacing, and dialogue that sounds like real people.

Sherpa's Prose Engine was designed specifically for storytelling. It was built on 1B+ tokens of Pocket's own stories, trained by learning from what listeners engage with, where they drop off, and what keeps them hooked.

Feedback is taken from specialized evaluator models that measure every scene against a 40-item checklist. (Evaluator models were benchmarked against human reviewers and matched them 80–90% of the time.)
_______________________________________________

Owning distribution and creation puts us in a very unique spot.
More shows -> More data -> Sherpa becomes better -> more creator success -> more creators -> more shows

Pocket FM has already seen one $100M IP. I believe Sherpa will soon lead to dozens of single-person studios creating billion-dollar shows.

Most people are scared of AI but I think it'll unlock more human creativity, help creators earn more, and bring the next great IPs to life. This will create millions of jobs and new income streams.

[Ronin @DeRonin_]

founder to founder

you should be watching how Ben Cera operates

polsia is a $250M company and he runs it while traveling the world

not after the exit

during

and before you call it a flex, look at what he's actually doing:

1. he treats rest as maintenance, not as a reward

most founders earn their rest, hit the milestone, then allow yourself a weekend

he takes it before he's empty, because the version of him who hasn't left his desk in five weeks makes worse calls, and one bad call costs more than a week off ever will

2. he says the quiet part out loud

founder twitter runs on 4 hours of sleep and a suffering complex

he's ambitious, intense, obviously obsessed, and he still says it plainly: leave, see friends, reset, come back sharper

that's not softness, that's someone who did the math on a ten year mission

3. he's building a company he can survive building

if polsia is meant to get massive, he can't operate like the next 6 weeks decide everything

the company needs an operating system that survives years, and so does the founder running it

what to take from this:

your output over 3 years matters more than your output this month

book the break before you need it, not after you break

perspective is an input to your decisions, not a prize for making them

most of the field quits by year 5, so staying sane is a competitive advantage

MUST WATCH ↓

[Ben Cera @Bencera]

Europe is the best place in the world to live and the worst place to be a founder.

Took a short break in Europe this Summer. First real one in 6 months.

Every meal here is a masterpiece: fresh, simple, real. A random café in Paris eats 99% of US restaurants alive.

Then you land back in the US and you start feeling the energy. The speed. The ambition. The stakes. The paranoia that someone, somewhere, is shipping faster than you.

America is the best country in the world to be a founder and the worst in the world to eat a healthy meal.

Pick your poison.

I filmed my whole experience.

aisloP episode 8: "The Break”, out now.

[🍯 @gpumaxxer]

i’m a sugar daddy for men with GitHub accounts.

Marc got $68k and a Mercedes. you get a shot at $50k for building something people actually use with the Higgsfield API.

competition pinned. make me proud.

[Ronin @DeRonin_]

@tspy Yeah, it made insane impression for me as well

[Ronin @DeRonin_]

4 things to do when you get access to Jev:

1. don't replace your model, put Jev in front of it

Jev makes the small choice first, and your expensive model only runs when it's really needed

one email tool swapped 2 AI calls per email for 1 Jev call, and kept all their normal safety checks

on a 120 ticket test: 42 seconds with Jev in front → 22 minutes without it
cost: $0.0003 → $0.059

the person who ran that test said his slow version was slowed down even more by retry errors, so the real gap is smaller

how to do it:

- look through your code for AI calls that only choose something and never write text
- write the list of possible answers in your own code, don't let the model invent them
- send that list to Jev, and keep your old call as a backup
- put it behind an on/off switch so you can undo it in one line
- run both for a week and compare them before you delete the old one

you're not rebuilding your product, you're replacing one call

2. ask 5 small questions instead of 1 big one

someone tested Jev on 2,000 phishing emails

ask it one big question and it gets 89.4%
a simple 2 line text rule gets 91.8%
Claude Haiku 4.5 asked the same question gets 94.2%

so when it has to give the final answer alone, Jev loses even to a text rule

then he asked 5 small questions instead, and added the answers up in his own code

95.0%, the best score in the whole test

SAME MODEL, SAME EMAILS

how to do it:

- take the big question you were about to ask
- write down the 5 things a person checks before they answer it
- ask each one separately, all in the same request, they run at the same time and cost almost nothing extra
- add the answers up in your own code, with your own weights
- test those weights on 100 examples where you already know the right answer
- change the order of your options and run it again, reordering 4 options changed 7 answers out of 120

Jev is good at noticing things and bad at making the final call

so keep the final call in your code

3. never ask Jev if it can answer

on 120 test tickets, any question like "do you have enough info?" said yes on 85% of them

a simple "need more info" flag said yes on 119 out of 120 tickets, and it then answered those tickets about 87% correctly

it doesn't know what it doesn't know

how to do it:

- delete any question like "can you answer this" or "is there enough context"
- make every option a real action your code can run
- always read the confidence score, not just the answer
- choose your limit by risk: low for reading data, 0.85+ for anything you cannot undo
- send everything below the limit to a human or to your big model

in that same test, using 0.8 as the limit passed 30 cases to a human, and 93% of them were passed for the right reason

4. run it quietly next to what you already have

there's already a langchain package published and a pydantic-ai adapter being reviewed, so this is closer to a settings change than a rebuild

how to do it:

- leave your current system in charge, it still makes every decision
- send the same input to Jev too, and throw its answer away
- save both answers plus Jev's confidence score into one table
- after a week, look only at the rows where the two disagreed
- switch over only for the cases where Jev was right

that list of disagreements becomes your test set, and your normal traffic builds it for free

and don't use it when you already have labelled data

if the question never changes and you have examples to train on, a small model you host yourself beats Jev on speed and price, and needs no API key at all

Jev wins when you have no labelled data and the question keeps changing

so use it where the list of answers is short and the question is boring

that's most of your agent anyway

everything above comes from other people's public tests, not from production, because the model is only 4 days old

i'm just sharing what i find while i test this and try to make the work in my own company faster and cheaper

tomorrow i'll show you what happened when i put Jev in front of the meta ads work we do for one big client

and most of you are still on the waitlist anyway

so start with step one, because it needs no access and no API key at all: find the calls in your code that were never writing tasks in the first place

gl

[Ronin @DeRonin_]

How to use Jev, and where it actually gives you the 100x:

setup takes 10 minutes:

1. join the waitlist, people are getting approved same day
🔗 https://t.co/uO7aescvbM

2. install the official skill so your agent writes correct calls:

- npx skills add typesafe-ai/skills --skill typesafe-ai

on Claude Code it's two commands, the marketplace add on its own doesn't install anything:

- claude plugin marketplace add typesafe-ai/skills
- claude plugin install typesafe@typesafe-ai

3. create an API key in the dashboard

4. in your prompt just say: "use the TypeSafe skill"

now the part nobody is posting:

the 100x isn't the model, it's where you put it

you don't get it by swapping your LLM for Jev
you get it by deleting the calls that never needed a language model

open your agent and find every call that just picks something:

> which tool next
> is this spam
> is this chunk relevant
> does this need a human
> is this diff risky

none of those are writing tasks
they're if statements you outsourced to a frontier model

here's the upgrade, in order:

1. replace each one with a typed question
Choice picks from up to 255 options, Score places it on a 2-10 level scale, Noul returns a raw 0-1

2. batch them
questions in one call run in parallel and barely move the latency, and output tokens are free
so ask every question you might need, including the ones you'll throw away

3. threshold on confidence, not on the answer
under 0.5 escalate to a big model or a human
0.85+ before anything irreversible

4. never let it invent options
build the candidate list in code, from the DOM, the retriever, the tool trace
then let it pick

5. put it in the loop, not next to it
router picks the cheap model, gate checks the tool call before it runs, judge verifies the output after
that's where the heaviest calls in your agent are hiding

6. start with compaction tonight
score every tool call, drop the dead ones, keep the survivors verbatim instead of a lossy summary
lowest effort win available and you'll see it on tomorrow's bill

the honest part:

text only right now, no images, no audio
and on broad benchmarks it loses to frontier models

but somebody ran 18,514 emails through it zero-shot and got 98.33%
against a TF-IDF classifier trained on 14,800 labelled examples that got 98.39%

no training data, $1.12 total

it wins on narrow, well specified decisions
which is most of what your agent is actually doing all day

today gonna share use case how i integrated it to content creation and how i find winning meta ads now in a seconds...

[Ronin @DeRonin_]

@n8mirai 🤡

[Ronin @DeRonin_]

How to use Jev, and where it actually gives you the 100x:

setup takes 10 minutes:

1. join the waitlist, people are getting approved same day
🔗 https://t.co/uO7aescvbM

2. install the official skill so your agent writes correct calls:

- npx skills add typesafe-ai/skills --skill typesafe-ai

on Claude Code it's two commands, the marketplace add on its own doesn't install anything:

- claude plugin marketplace add typesafe-ai/skills
- claude plugin install typesafe@typesafe-ai

3. create an API key in the dashboard

4. in your prompt just say: "use the TypeSafe skill"

now the part nobody is posting:

the 100x isn't the model, it's where you put it

you don't get it by swapping your LLM for Jev
you get it by deleting the calls that never needed a language model

open your agent and find every call that just picks something:

> which tool next
> is this spam
> is this chunk relevant
> does this need a human
> is this diff risky

none of those are writing tasks
they're if statements you outsourced to a frontier model

here's the upgrade, in order:

1. replace each one with a typed question
Choice picks from up to 255 options, Score places it on a 2-10 level scale, Noul returns a raw 0-1

2. batch them
questions in one call run in parallel and barely move the latency, and output tokens are free
so ask every question you might need, including the ones you'll throw away

3. threshold on confidence, not on the answer
under 0.5 escalate to a big model or a human
0.85+ before anything irreversible

4. never let it invent options
build the candidate list in code, from the DOM, the retriever, the tool trace
then let it pick

5. put it in the loop, not next to it
router picks the cheap model, gate checks the tool call before it runs, judge verifies the output after
that's where the heaviest calls in your agent are hiding

6. start with compaction tonight
score every tool call, drop the dead ones, keep the survivors verbatim instead of a lossy summary
lowest effort win available and you'll see it on tomorrow's bill

the honest part:

text only right now, no images, no audio
and on broad benchmarks it loses to frontier models

but somebody ran 18,514 emails through it zero-shot and got 98.33%
against a TF-IDF classifier trained on 14,800 labelled examples that got 98.39%

no training data, $1.12 total

it wins on narrow, well specified decisions
which is most of what your agent is actually doing all day

today gonna share use case how i integrated it to content creation and how i find winning meta ads now in a seconds...

[tamara @tamarajtran]

found the perfect use case for @typesafeai Jev:

instant compaction

in 2026, why is compaction still a summarization prompt?

Jev can make it instant by scoring every tool call and dropping what’s irrelevant

[ZEUS⚡️ @zeuuss_01]

https://t.co/ZGCYNqZget

[Ronin @DeRonin_]

@ishgineering @duino_ai interesting to be a co-founder ahah

are u open for this?

[Ronin @DeRonin_]

@VadimStrizheus Jev grinder, got you man

[Ronin @DeRonin_]

@traveldreamerr weird, since i've been approved on few my alts for few hrs

without mentioning that i am creator or something like this, just a normie

[Ronin @DeRonin_]

@marclou so proud to be a part of Higgsfield 🤝

[Ronin @DeRonin_]

@ah4alt KV caching is a game changer with Jev basically

was testing it for the whole morning and I am so curious what could build with this