EngineerBetter

I made something very similar, in my spare time, last week. I didn't need $60m for it.

https://github.com/re-cinq/claudit

What's amusing is that I specifically made a comedy license for it, because I thought trying to protect the IP of such a thing is madness in the AI-native era. Then within days I find out it's a massively funded startup!

https://github.com/re-cinq/ai-native-application-license/

I've got Claude Code adding Gemini and OpenCode support to my tool currently.

I made this last week in my spare time, without $60m in seed funding!

https://github.com/re-cinq/claudit

Yes. Admittedly they both work in bars/breweries.

It'd be interesting to see the experience of folks in continental Europe, where I gather wine with meals is much more commonplace.

It's worth pointing out that AWS does now support U2F, which isn't reflected in the posts.

Apologies - I updated the post today to include the commit signing, and didn't think about the date of the parent post.

I'm a very strong Concourse proponent. We've been using it exclusively for three years, and it's better than anything we've experienced that our customers use (I run a consultancy).

We run Concourse London User Group, and the first few instances were like some kind of Jenkins survivor's support group.

It also makes things more comfortable - you can stretch and change position on long drives. In three hours of motorway driving on Sunday, I was only driving for about three minutes - the rest was Autopilot.

I drove from the UK to Ålesund, Norway and back in a Model X, which was about 1,300 miles in each direction. Did it on the way home with two kids in the back.

Long trips in a Tesla are totally doable.

> If you could actually predict the future you should invest in the lottery, not in software.

Hear, hear!

Estimates seem to be a common topic in replies.

If you can accurately estimate how long a software task will take, you should have already made a reusable component or automation to generate that code.

Estimates are meaningless. I've seen PhDs waste endless hours faffing with estimate-calculating spreadsheets.

Fundamentally, the universe is unpredictable. Chaotic and complex systems require their starting conditions to be measured to an infinite level of precision to be predictable. The Heisenberg principle means this is, as far as physics can tell, impossible.

On a more practical macro level, a complex adaptive system becomes unpredictable once three feedback loops are present (the three body problem is related).

Modern computer systems are unpredictable because we cannot predict the interplay between levels of abstraction.

It does not matter how smart you are. Unless you have perfect knowledge of all levels of abstraction in the system below you, even in a macroscopic sense you cannot predict the future. Precise estimates will be inaccurate.

Confidence ranges and superprobabilities are of some use. Discrete and precise estimates are an utter waste of time at best, and are dangerous and misleading at worst.

EDIT

Written on a mobile waiting for plane take-off, hence lack of citations. For more on complex adaptive systems, I recommend the works of Murray Gell-Mann and the research output of the Santa Fe Institute, particularly Scott E. Page.

Indeed, and the Scrum-Industrial Complex has vested interests in codifying a certain format that it can sell.

If you're getting burnout doing agile, you're doing agile wrong.

Don't do sprints. Have a continuous backlog. Don't do overtime. Don't make estimates. Always do the simplest thing. Only ever do the most important thing, as defined by the stakeholder.

I've written and talked about this at great length. The fact people suggest agile gives you burnout reinforces my experience that Scrum is largely misinterpreted and people incorrectly focus on sprint commitments. If Scrum is so commonly misinterpreted, it is flawed.

https://www.linkedin.com/pulse/scrum-makes-you-dumb-daniel-j...

https://youtu.be/k9duArRuSjQ

This isn't strictly true - Teslas have the ability to perform emergency stops if they detect something in front of them.

I don't believe that they're likely to pick up something driving straight at them, as it's not something the neural net has been trained for.

My Model X often alerts me to parked cars, thinking I might drive into them :P

I saw these folks at AfrotechFest[1] in London last year, which one of my colleagues helped organise, talking about their entrepreneurial experience. Really great to see them getting mainstream media coverage, and I wish them all the best. The idea of getting into a van full of blades with strangers was definitely joked about on stage, and if they can salve that concern then they must be doing a good job!

https://www.afrotechfest.co.uk/

I'd highly recommend Arieh Ben-Naim's books on entropy for anyone looking for more on this point.

TLDR he makes the argument that entropy is a measure of the probability of probability distributions.

Wow, it takes something to make Philips Hue look cheap. I'll stick to those, which I can fill several rooms with for the same price.

Wow. 1,500C to -150C in 1/100th of a second sounds pretty remarkable. Any idea how that's achieved?

Ha, my sole experience of RDF was on an EU-funded research project :)

Yeah, accept those writes and favour availability. Customer was a wealth management company.

Imagine a customer has £100 in their account. System partitions. Customer withdraws £70, hitting one DC. Customer then hits the second DC, this time withdrawing £50. Each DC thinks the transaction is valid, and so serves it.

Later when the partition is restored, events are played back, and divergent history is detected via the vector clocks - the two withdrawals are not causally related. Remediative action can then be taken.

Transactions prevent bad things happening, but require CP semantics. Eventual consistency allows AP, allows bad things to happen, meaning you have to be able to detect them and clean them up later.

What do you do in a network partition?

Edit - yep, the event producer manages and increments the vector clock.

---

I've not really used Kafka, so couldn't comment on that. I did some work for a customer that involved multi-DC microservices with isolated databases (ie one DB per DC). We used event sourcing with vector clocks to do manual reconciliation of the databases including during partition. Reconciliation involved custom logic depending on the event type, so not sure how a transport mechanism like Kafka would handle that.

A talk about the approach is here: https://youtu.be/hYVh8PbbeJw

I get to vector clocks at the end.

I think this misses the main one - append vector clocks because your events will inevitably turn up out of sequence at some point.

It's no more a distraction than opening a drink, taking a swig of said drink, or having to look around to see why the kids are crying in the back of the car.

Let's not suggest that Autopilot is safe completely unsupervised, and let's also not suggest that all drivers always look at the road 100% of every drive they take. There is a realistic middle ground, and I'm very grateful to have Autopilot to be an extra set of eyes.

It's definitely a distraction from the road, but you can see the road whilst typing away with one hand.

I've got a Model X, and the touchscreen is only dangerous when using the on-screen keyboard. If I need to type something whilst driving, I engage Autopilot. There is voice recognition for the phone connectivity and Spotify, but I rarely use it.

Turning on windscreen heater, heating, and all that stuff is pretty straightforward, the on-screen buttons are always in the same place and are next to the bezel so you can anchor your hand. Plus cabin heating can be controlled via clickwheels on the steering wheel.

I think maybe the seat heaters are a little distracting. You can only tell their state by looking at the screen, and they cycle through settings. That's something I'd like a button for, or at least to be able to control via the clickwheels.

Edit

Studied HCI and Usability Engineering at university, did my dissertation in usability of web systems, and generally share your concerns. Not aiming to dismiss them, more say that in my experience it's not as bad as one might expect.

The big missing piece is not LIDAR, but causal reasoning. Autopilot and similar 'AI' cannot reason; it doesn't have a mental model to ponder 'what if' and can't use counter-factuals to ponder what would happen if it didn't do something. It's just glorified pattern matching currently.

Reading Judea Pearl's The Book Of Why certainly sobered my outlook on AI.

Has anyone outside of Porsche witnessed it charging at that rate?