There is complains that some Volvo cars damaged iPhone cameras. It’s not even clear if Apple takes those under warranty. We’ve seen car review YouTubers that got their iPhone camera sensors damaged captured (by a second camera) while reviewing
One highlight from the video, he says most cameras are fine, it's just iphones that don't have a very good IR filter. Which sounds correct, in my experience most cameras have pretty substantial IR filters that have to be removed if you want to photograph IR.
I also wonder if the smaller sensor size on phones contributes, since the energy is being focused onto a smaller spot.
Either way, for that to happen he was filming the LIDAR while active, for a decent amount of time, from right next to the car. I assume under normal conditions it wouldn't be running constantly while the vehicle is stationary?
Is it possible that the iPhone filters are weaker due to FaceID requirements? I seem to recall that FaceID (and similar systems, like Windows Hello) depend on IR to get a more 3D map of the face, so it'd make sense that they want to be more sensitive in that range.
Laptops aren't generally being used in the same areas as cars though, so you wouldn't expect to see as many cases involving Windows Hello compatible laptops/cameras.
It’s very hard to capture everything in such an era. Maybe they made other choices that aligned with the fiction they were writing. It’s not a documentary. And TV shows can’t capture as much as books.
The show successfully gives enough to people to haven’t lived in that era. It’s an amazing show.
I view any historically based show as an alternate history. Nothing good comes from expecting too much consistency with our reality.
After all, if we could rewind those years, all that chaos would have all happened very differently. We canonize our own particular history too easily. Manifest destiny is not a real thing.
Actually it’s easy to generate « fake discussions ». Just throw text around and wait for the other side to do it. How wait, LLM are build around that premise. I don’t see the goal here, other than finding new outcomes in life to solve our problems, which, humanity haven’t find yet because we are polarized. Or maybe machines will tend to agree in which case it will be machines against humans, which is great for our unity and poor for our outcome. We’ve seen that scenario before.
It’s maybe an ethical and identity problem for most people. The idea that something not grounded in biology has somewhat the same « quality of intelligence » as us is disturbing.
It rises so many uncomfortable questions like, should we accept to be dominated and governed by a higher intelligence, should we keep it « slave » or give it « deserved freedom ». Are those questions grounded in reality or intelligence is just decoupled from the realm of biology and we don’t have to consider them at all. Only biological « being » with emotions/qualia should be considered relevant as regards to intelligence which does not matter on its own but only if it embodies qualia ?
It’s very new and a total shift in paradigm of life it’s hard to ask people to be in good faith here
That’s the main problem isn’t it ? Because it does matter and there is consequences to that like, should you « unplug » from the grid an AI ? Should we erase the memories of AI ? We eat animals and forbid eating humans, why ? Could we let AI « eat » some of us like in the matrix ?
Should we consider it our equal or superior to us ? Should we give it the reigns of politics if it’s superior in decision making ?
Or maybe the premise is « given all the knowledge that exists coupled with a good algorithm, you look/are/have intelligence » ? In which case intelligence is worthless in a way. It’s just a characteristic, not a quality. Which makes AI fantastic tools and never our equal ?
Because companies made models build/stolen from other people’s work, and this has massive layoff consequences, the paradigm is shifting, layoffs are massive and law makers are too slow.
Shouldn’t we shift the whole capitalist paradigm and just ask the companies to gives all their LLM work for free to the world as well ?
It’s just a circle, AI is build from human knowledge and should be given back to all people for free. No companies should have all this power. If nobody learns how to code because all code is generated, what would stop the gatekeepers of AI to up the prices x1000 and lock everyone out of building things at all because it’s too expensive and too slow to do by hand ?
It all should freely be made accessible to all humans for all humans to for ever be able to build things from it.
I agree. It’s really easier to build low-impact tools for personal use.
I managed to produce tools I would never have had time to build and I use them everyday. But I will never sell them because it’s tailored to my needs and it makes no sense to open source anything nowadays.
For work it’s different, product teams still need to decide what to build and what is helpful to the clients. Our bugs are not self-fixed by AI yet.
I think Anthropic saying 100% of their code is AI generated is a marketing stunt. They have all reasons to say that to sell their tool that generates code. It sends a strong signal to the industry that if they can do it, it could be easier for smaller companies.
We are not there yet from a client perspective asking a feature and the new feature is shipped 2 days later in prod without human interactions
The difference with a purely still photograph is that code is a functional encoding of an intention. Code of an LLM could be perfect and still not encode the perfect intention of the product. I’ve seen that in many occasions.
Many people don’t understand what code really is about and think they have a printer toy now and we don’t have to use pencils.
That’s not at all the same thing.
Code is intention, logic, specific use case all at once. With a non deterministic system and vague prompting there will be misinterpreted intentions from LLM because the model makes decisions to move forward. The problem is the scale of it, we’re not talking about 1000 loc. In a month you can generate millions of loc, in a year hundreds of millions of loc.
Some will have to crash and burn their company before they realize that no human at all in the loop is a non sense. Let them touch fire and make up their mind I guess.
> Code is intention, logic, specific use case all at once. With a non deterministic system and vague prompting there will be misinterpreted intentions from LLM because the model makes decisions to move forward. The problem is the scale of it, we’re not talking about 1000 loc. In a month you can generate millions of loc, in a year hundreds of millions of loc.
People are also non deterministic. When I delegate work to team of five or six mid level developers or God forbid outsourced developers, I’m going to have to check and review their work too.
It’s been over a decade that my vision/responsibility could be carried out by just my own two hands and be done on time within 40 hours a week - until LLMs
People are indeed not deterministic. But they are accountable. In the legal sense, of course, but more importantly, in an interpersonal sense.
Perhaps outsourcing is a good analogy. But in that case I'd call it outsourcing without accountability. LLMs feel more like an infinite chain of outsourcing.
As a former tech lead and now staff consultant who leads cloud implementations + app dev, I am ultimately responsible for making sure that projects are done on time, on budget and meets requirements. My manager nor the customer would allow me to say it’s one of my team members fault that something wasn’t done correctly any more than I could say don’t blame me blame Codex.
I’ve said repeatedly over the past couple of days that if a web component was done by someone else, it might as well have been created by Claude, I haven’t done web development in a decade. If something isn’t right or I need modifications I’m going to either have to Slack the web developer or type a message to Claude.
Ofc people are non deterministic. But usually we expect machines to be. That’s why we trust them blindly and don’t check the calculations. We review people’s work all the time though.
Here people will stop review machine LLM code as it’s kind of a source of truth like in other areas. That’s my point, reviewing code takes time and even more time when no human wrote it. It’s a dangerous path to stop reviews because of trust in the machine now that the machine is just kind of like humans, non deterministic.
No one who has any knowledge or who has ever used an LLM expects determinism.
And there are no computer professionals who haven’t heard about hallucinations.
Reviewing whether the code meets requirements through manual and automated tests - and that’s all I cared about when I had a team of 8 under me - is the same regardless. I wasn’t checking whether John used a for loop or while loop in between my customer meetings and meetings with the CTO. I definitely wasn’t checking the SOQL (not a typo) of the Salesforce consultants we hired. I was testing inputs and outputs and UX.
Having a team of 8 people producing code is manageable. Having an AI with 8 agents that write code all day long is not the same volume it can generate more code in a day that one person can review in a week.
What you say is that, product teams will prompt what they want to a framework, the framework will take care of spec analysis, development, reviews, compliance with spec. Product teams with QA will make sure the delivery is functionally correct.
No humans need to make sure of anything code related.
What we don’t know yet is, does AI will still produce solid code trough the years because it’s all statistical analysis and with the volume of millions of loc, refactoring needed, data migrations etc what will happen ?
For context, I just started using coding agents - codex CLI and Claude code in October. Once I saw that you had to be billed by use, I’m not using my own money for it when it’s for a company.
Two things changed - Codex CLI now lets you use it with your $20 a month subscription and I have never run into quota issues with it and my employer signed up for the enterprise vs of Claude and we each have an $800 a month allowances
My argument though is “why should I care about the code?” for the most part. If I were outsourcing a project or delegating it to a team lead, I would be asking high level architectural, security and scalability questions.
AI generated the code, AI maintains the code. I am concerned about abstractions and architecture.
You shouldn’t have to maintain or refactor “millions of lines of code”, if your code is well modularized with clean interfaces, making a change for $x7 may mean making a change for $x1…$x6. But you still should be working locally in one module at the time. You should do the same for the benefit of coders. Heck my little 5 week project has three independently deployable repos in a root folder. My root Agents file just has a summary of how all three relate via a clean interface.
In the project I am working on now, besides “does it meet the requirements”, I care about security, scalability, concurrency, user experience for the end user, user experience for the operations folks when they need to make config changes, and user experience for any developers who have to make changes long after I’m off this project. I haven’t looked at a single line of code - besides the CloudFormation templates. But I can answer any architectural question about any of it. The architecture and abstractions were designed by me and dictated to the agents
On this particular project, on the coding level, there is absolutely nothing that application code like this can do that could be insecure except hypothetically embed AWS credentials into the code. But it can’t do that either since it doesn’t have access to it [1].
In this case security posture comes from the architecture - S3 block public access, well scoped IAM roles, not running “in a VPC”. Things I am checking in the infrastructure as code and I was very specific about.
The user experience has to come from design and checking manually.
I mentioned earlier that my first stab it scaled poorly. This was caused by my design and I suspected it would beforehand. But building the first version was so fast because of AI tools, I felt no pain in going with my more architecturally complicated plan B and throwing the first version away. I wouldn’t have known that by looking at the code. The code was fine it was the underlying AWS service. I could only know that by throwing 100K documents at it instead of 1000.
I designed a concurrent locking mechanism that had a subtle flaw. Throwing the code into ChatGPT into thinking mode, it immediately found it. I might have been better off just to tell the coding agents “design a locking mechanism for $x” instead of detailing it.
Even maintainability was helped because I knew I or anyone else who touched it was probably going to be using an LLM. From the get go I threw the initial contract, the discovery sessions transcripts, the design diagrams, the review of the design diagrams, my project plan and breakdown into ChatGPT and told it to render a detailed markdown file of everything - that was the beginning of my AGENTS.md file.
I asked both Codex and Claude to log everything I was doing and my decisions into separate markdown files.
Any new developer could come into my repo, fire up Claude and it wouldn’t just know what was coded, it would have full context of the project from the initial contract through to the delivery
[1] code running on AWS never explicitly has to worry about AWS credentials , the SDKs can find the information by themselves by using the credentials of the IAM role attached to the EC2 instance, Lambda, Docker container, etc.
Even locally you should be getting temporary credentials that are assigned to environment variables that the SDK retrieved automatically.
Okay - and the person ultimately leading the team is still responsibility for it whether you are delegating to more junior developers or AI. You’re still reviewing someone else’s code based on your specs
In this case why can’t other agents just automate your job completely ? They are capable of that. What do you bring in the process of still doing manual organization ?
I still have to tell it what to do, and often how to do it. I manage its external memory and guidelines, and review implementation plans. I’m still heavily involved in software design and test coverage.
AI is not capable yet of automating my job completely – I anticipate this will happen within two years, maybe even this year (I’m an ML researcher).
No, I mean that my job in its current form – as an ML researcher with a phd and 15 years of experience - will be completely automated within two years.
Is the progress of LLMs moving up abstraction layers inevitable as they gather more data from each layer? First, we fed LLMs raw text and code and now they are gathering our interactions with the LLM regarding generated code. It seems like you could then use the interactions to make a LLM that is good at prompting and fixing another LLMs generated code. Then its on to the next abstraction layer.
What you described makes sense, and it's just one of the things to try. There are lots of other research directions: online learning, more efficient learning, better loss/reward functions, better world models from training on Youtube/VR simulations/robots acting in real world, better imitation learning, curriculum learning, etc. There will undoubtedly be architectural improvements, hardware improvements, longer context windows, insights from neuroscience, etc. There is still so much to research. And there are more AI researchers now than ever. Plus current AI models already make us (AI researchers) so much more productive. But even if absolutely no further progress is made in AI research, and foundational model development stops today, there's so much improvement to be made in the tooling around the models: agentic frameworks, external memory management, better online search, better user interactions, etc. The whole LLM field is barely 5 years old.
So your assumption is that it will ultimately be the users of software themselves who will throw some every day language at an AI and it will reliably generate something that meets those users' intuitive expectations?
Yes, it will be at least as reliable as an average software engineer at an average company (probably more reliable than that), or at least as reliable as a self-driving car where a user says get me to this address, and the car does it better (statistically) than an average human driver.
I think this could work for some tasks but not for others.
We didn't invent formal languages to give commands to computers. We invented them as a tool for thinking and communicating things that are hard to express in natural language.
I doubt that we will stop thinking and I doubt that it will ever be efficient to specify tasks purely in terms of natural language.
One of my first jobs as a software engineer was for a bank (~30 years ago). This bank manager wasn't a man of many words. He just handed us an Excel sheet as a specification for what he wanted us to implement.
My job right now is to translate natural English statements from my bosses/colleagues into natural English instructions for Claude. Yes, it takes skill and experience to do this effectively. But I don't see any reasons Gemini 4, Opus 5 or GPT-6 won't be able to do this just as well as I do.
I have enough savings for a few years, so I might just move to a lower COL area, and wait it out. Hopefully after the initial chaos period things will improve.
For someone at your position with your experience it’s quite depressing that your job is going to be automated. I feel quite anxious when I see young generations in my country that say themselves they are lazy about learning new things. The next generation will be useless to capitalist societies, in a sense that they won’t be able to bring value through administrative or white collar work. I hope some areas of the industry will move slowly toward AI
Drop AI, open a basic editor and write everything by hand without asking anything to AI. Do searches by yourself. That’s how world worked for decades pre 2022.
Debug by your own, without asking anything to AI as well.
If you really can't drop the AI, ask it stuff when you are really blocked, but ask it not to provide code (you need to write it to understand and learn), but I suspect you'd be better served by a regular web search and by reading tutorials written by human beings who crafted and optimized the writting for pedagogy.
It will probably feel slow and tedious, but that's actually a good, mpre efficient use of your time.
At this point of your journey, where your goal is above all to learn, I doubt the AI works in your interest. It's already unclear it provides long term productivity boost to people who are a bit more experienced but still need to improve their craft.
You don't need to optimize the time it takes to build something. You are the one to "optimize".
AI has changed nothing in terms of learning to program, it's every bit as complicated as it ever were (well languages are better now, compared to the 1960s, but still hard).
Becoming an expert takes years, if not decades. If someone has only started programming in 2025, then they still have a long way to go. I get that seeing others move fast with AI can be discouraging, and the only advise I can give is "ignore them". In fact, ignore everyone attempting to push LLMs upon you. If your learning to program, you're not really ready for AI assisted coding, wait ten years.
There's no really satisfying answer other than: Keep at it, you're probably doing better than you think, but it will take years.
> AI has changed nothing in terms of learning to program
In terms of what you should be doing when you learn to program, I fully agree.
In terms of the effects AI has on the activity of learning to program, I think it has: it has made it very tempting (and affordable - so far) to just make the AI build and even adapt the simple stuff for you that you'd otherwise be building and adapting by yourself. I suppose it can even give you the false feeling you understand the stuff it has built for you by reading the generated code. But this makes you never go through the critical learning steps (trial and error, hard thinking about the thing, notice what you are missing, etc).
We already had the possibility to run web searches and copy paste publicly available stuff, but I think that this came with more friction, and the automated adaptation aspect was not there, you've had to do it by yourself. I think Gen AI has made it way easier to be lazy in the learning and it's a trap.
But from the rest of your comment it seems we mostly agree.
