_fat_santa

Where I found MCP's really useful is integrating with "consumer AI" (chatgpt.com, claude.ai, etc).

I'm working on a sideproject called Rowbly[1]. It acts as a sharable data store where LLM's can dump research rather than keeping it in their memory or throwing it into a spreadsheet.

At first I thought "I don't need an MCP, I'll just expose a CLI" but that carries a pretty big limitation in that it only works with agents on your computer (Codex, Claude Code, Pi, etc). For the folks on here this is not an issue and is often times preferable but I'm also targeting the average LLM user that primarily interfaces with it via "consumer AI" and with those tools the stuff you can do is very very limited.

I still think MCP's have a long way to go maturity wise and hey maybe in a few years we will figure out a better way to do things but for now, if you want to interact with consumer AI apps, there's just no way around using them.

[1]: https://rowbly.com

I feel like the "We hacked hugging face" thing and this influencer thing are targeting different things but have the same goal of hyping up the company for the IPO.

- The HF incident was to get the attention of investors and convey the message: "we have this incredibly powerful technology, imagine where we will be in 5 years"

- With this influencer campaign it's just a drive to get more people to use ChatGPT / sign up to ChatGPT so when IPO time comes, they can show strong user growth.

Over the past few months I've seen various reports that OpenAI's financial position isn't great and if those reports have any merit, then likely the higher ups at OpenAI are panicking that they might become a WeWork 2.0. Everything they are doing now is an attempt to justify their $1T valuation to investors during the IPO.

> The trillion dollar question is how you do this

I don't think it's that hard of a question to answer. I've noticed on my team, our thinking has shifted from how do you directly solve a problem, to how you get an agent to effectively solve the problem and not produce slop in the process.

One thing that we have done that's probably made the biggest impact is alot more upfront architecture with the knowledge that pretty soon agents will be running wild all over the code. Having worked with these agents for a while now, you get a very good sense of how they will go about solving a problem and the various footguns they will encounter along the way. Editing an AGENTS.md file or building a skill is not nearly as fun as coding by hand but it will pay dividends over and over if you do it right.

Another big thing is doing refactoring passes. Early on in our projects our agents generated ALOT of slop and we had to go back and fix alot of it. But every time we did one of these passes, a major aspect was improving agent instructions / skills / etc so it doesn't happen again. It can be a painful process at first but I found that over time, the amount of slop the agent produces goes down by orders of magnitude.

I feel like we're still very much programming, but we're now doing it at a "higher level" where we are not writing the code ourselves but instructing the agent to. And IMHO, properly instructing an agent on a production codebase is not a trivial task.

I think that's past the point OP was making. Amazon makes some pretty impressive guarantees of data durability but those guarantees only hold up if you take their advice and replicate data to other regions.

It's like saying "Oh this car manufacturer claimed that their cars were the safest in the industry but couldn't prevent this driver from flying through the windshield" and omitting the fact the person wasn't wearing a seatbelt.

They would have to encrypt it in such a way where even if Law Enforcement went to apple with a subpoena, they would not be able to hand over that data at a technical level.

The better way IMHO is use local models so data never actually leaves the device.

At least with Codex, this has not been my experience at all. It still screws up sure, but in every case I can ask "why did you do this" and it can trace back what made it take that particular decision. Typically it's always that I either didn't specify the problem correctly or made a really dumb mistake (executing the task on the wrong project....did this one yesterday) or it's something within a skill file that instructs it (at which point I fixup the instructions).

Once in a blue moon it's actually the model making a material error in it's thinking and I have to go back and redo it.

Part of the issue is many TV's these days are essentially bricked until you connect them to the internet to "activate".

I bought a Hisense TV a few years back and when I set it up, I could not proceed without downloading the companion app. I was curious so I called support and told them that I didn't have a phone and asked how I could activate the TV.

Support had no answer for me other than "well you need the app". When I told them I didn't have a phone to install it on they just said "no that's not correct, you must have a phone..."

This is just the 2020's version of "StackOverfow is down"

I've personally adopted doing multiple refactoring passes after any large code implementation done by AI. In pretty much any scenario where I'm adding code using AI, it's 1 turn to add the feature and and then another 4-5 turns to refactor and clean everything up.

Often times it's not even that the code is bad but rather that it's overengineered. I see it happen so much that I'm tempted to actually go the other way on a toy project. Like what would Claude or Codex come up with if I told it I wanted an enterprise grade, globally scalable, compliant and auditable tic-tac-toe game.

I just switched over to it from Ubuntu. So far the nice thing is that it gives you a fully decked out hyprland setup without any of the hassle and pretty good UX.

The problem I've always had with trying out a tiling window manager like hyprland is you're going to spend a very long time trying to get everything just right. With Omarchy I get a really nice hyprland setup right out of the box.

I agree, presentation is a big issue with PWA's since you have to know how to add it vs just searching in the app store. I'm sure were going to get a native app eventually, though customers have not asked for one yet and seem to be pretty happy with the PWA.

A little off topic but for anyone using Thunderbird, I highly recommend BetterUnsubscribe[1] to help combat spam. Since installing I've basically eliminated marketing emails from my inbox. I've used tools like unroll.me in the past but I found that a one-time review doesn't really help much since every signup and every form entry always risks you getting more marketing emails and having to go to another service all the time creates alot of friction. That extension lets you unsubscribe the moment you see a marketing email hit your inbox.

[1]: https://github.com/LucBennett/BetterUnsubscribe

You must be following the wrong people.

This is a constant argument i have with by business partner. He's always asking me "when are we making an app for our <SaaS>" and I always reply back: "We already have an app! It's just a PWA"

PWA's are freaking awesome for both users and developers. Users don't have to install a massive app and me as a developer can focus on making one platform better rather than 3.

And they are pretty freaking powerful in what you can do. Not to toot my own horn too much but I created an "app"[1] for viewing cities on the other side of the earth from where you're standing. It's got camera, gyro and GPS to determine what you're looking at to the folks I've shown it too, anyone non-technical has been completely fooled and thought it was a proper app.

[1]: https://earthview.sunny.gg

When it comes to the AI backlash, I think a big problem is that we have absolute terrible messengers and they are spreading terrible messages.

If I go on X, the vibe there around AI is amazing. You have folks constantly talking about cool things that they get their agents to do. Agents can help you make things you didn't have the knowhow to make, teach you things at a level that you couldn't get just by searching Google and take care of the boring parts of your life so you can spend more time doing the things you care about. Overall things are very positive and makes you excited for the future.

Contrast that with the public comments by AI executives. Pretty much all of the messages fall flat at best or are just outright doomery at worst. It's such a stark contrast and while I'm personally really excited for the future, I can totally see how someone who isn't plugged into that world to get a very grim view of the future.

And even when they run ads about AI, it's always the worst examples (thinking of that Gemini ad that talked about writing a letter to your family with AI). I really wish the industry could have a better messenger around this stuff. Don't talk about how it can help you do stuff you already know how to do, run an ad about how it can enable you to do stuff you couldn't do before.

I think the best example of this is a bike mechanic fixing a Reevo bike and writing firmware using Claude Code so it no longer required an app to work[1]. The author outright said he didn't know how to code or do anything in that area but was able to figure it out with the help of AI. I think a message like that around AI is awesome but no all we hear about is how AI will take your job.

[1]: https://www.youtube.com/watch?v=hPrtVGimBYs

This has largely been my experience. On the one hand I now have AI writing 99% of my code. On the other hand I'm not dealing with a whole new class of problems that come out of that.

I did a comparison between Opus 5 and Sol and the results were night and day. Basically took a feature in my app and asked it to explain to me how it works.

Sol gave me a straightforward bulleted list, easy to scan and read. Opus 5 though....it gave me a solid 7 paragraphs of how it worked. I read through it and yeah it nailed the same points Sol did but the output was way harder to read.

I've heard the same thing about Bloomberg's chat many years ago. Since everyone you're chatting to also has to fork over ~$30k/yr to use it, there's the implicit assumption that you're talking to a serious person.

My team is currently developing our 3rd product this year where 99% of the code is written by an agent. One thing I've definitely noticed is our problem solving hasn't stopped, it just moved up the stack to the "agent layer".

A big part of this is ensuring that a "bad" engineers can still write solid code and also investing in systems that make it easier for us to review code.

When it comes to writing code, we have this entire library of coding standards that we've moved from one project to another. It describes, sometimes in excruciating detail, exactly how we want our code structured, antipatterns, best practices, etc.

On the review side we have invested equally into skills that split up code into readable chunks, take screenshots of any UI changes for quick validation, and a whole battery of tests to ensure that we're not generating slop.

If you were to look at just our development process, you would conclude that we're very lazy engineers. We seldom write code by hand, we seldom ask for corrections and our reviews are more of a cursory look at the PR rather than a deep review.

But the real work is not in the "development layer", it's now in the "agent layer". Making sure the agent knows how to write solid code so we don't need to write code by hand, making sure it doesn't make dumb mistakes so we don't have to correct it, and structuring our review process in such a way where an engineer only has to take a cursory look at the code.

The key difference we noticed between the "old way" and the "new way" is the "new way" is way more scalable and we're able to move way faster than we ever could before.

I don't find the "AI will cause mass unemployment" argument compelling, in fact I see the opposite as being true.

The reason we are talking so much about that now is that at the current level of productivity, we just don't need as many humans to do the work, as companies can use AI as leverage to not hire as many people.

This i feel will be a short lived period where not everyone has adopted AI to the fullest and thus simply having AI gives you leverage to not hire as many people and still stay competitive. The same thing happened with computers writ large, during the early days the folks that used computers had massive leverage over those that did not.

But what happens in 5-10 years once most orgs standardize on that leverage and everyone has it. All of a sudden that advantage AI gave you over others evaporates and you're right back to how things have always been, that is more humans give you more leverage.

I think once AI is broadly adopted and standardized, there will be an insane boom period for workers.

> Building a Saas product seems somewhat trivial at this point.

The trap here is the word "seems". When I started building my SaaS years back I thought the problem space was trivial (even joked to my business partner that I could spin the whole thing up in a weekend).

But almost 5 years on it's anything but trivial. The issue isn't with the core features, the core features in our app have been running for years without issue. What is very much non-trivial are all those subtle tiny features around the main feature that enhance the experience.

My customers don't pay me for the primary feature, that they could reproduce in a weekend. They pay me for all the tiny sub-features that came out of all those minor things that I mentioned in my first comment.

Those tiny details are the reason that it's both very easy to say things like "SaaS is trivial" and why the entrenched players are still at the top.

> If AI can build anything. Why do you need a human in the loop?

I'm currently building a SaaS with my business partner. I'm the "tech guy" while he has decades of industry experience with the industry that our SaaS is targeting.

What I found building this is there is a giant subset of preferences, problems, and other minor details that aren't written down anywhere, they are just kept in the minds of the people in that industry.

The reason it's not written down is to those people, these things seem obvious. And these things are both extremely important but also very subtle.

We use AI heavily in designing our application but there's always a testing pass where my partner goes over some changes and calls out subtle things of how "things work in practice". These things aren't in any manuals, they are seldom even written down and thus AI systems are completely oblivious to these details.

Now you could build a system that skips some of these subtleties and still gets the job done but what you will find is potential customers will always return to their existing system that takes these into account.

Now one day we might get an AI system that detects these subtleties but in order to do that you would need a load of training data around those things. But how do you even get that training data if all of it is held inside the minds of the people working in that industry?

It's useful for running multiple long running tasks alongside your "primary" work. As an example I currently have 3 tabs open running a PR review, 1 tab running "autonomous development" for a small time feature and then my "primary" window for working on a feature.

At my org we've been building AI agents and one internal rule we have is to use at most 50% of the models context window with the recommendation to not go over 25% for large context window models.

Anytime I see a "1M Context Window", my brain always goes "Gotcha so a 250k usable window"

Another commenter posted the complaint: https://www.documentcloud.org/documents/28513012-samuel-tuni...

The terrifying line of this for me is: "before and during the search", namely the "before" part. Even if you wiped your device days before, they could always make the argument that you destroyed evidence back then because you were trying to prevent them from searching your device.

INAL so not sure if this would hold up in court.

> The fundamental problem with the internet is that hosting sucks and no one wants to do it. It's thankless and it's expensive to maintain, both time and money. Apps are a way to not worry about that.

No it's not. Hosting a web app is one of the most trivial things you can do these days, far more trivial than attempting to get an app into the app store. Hosting API's and Databases is a little more difficult but you still need those things if you're building an app.

There is no world in which getting your app signed, getting it approved, getting every update approved and paying $X/year to Apple or Google is easier than hosting a webapp, even if you host it in the most difficult way possible (on say AWS + Cloudfront). And even that method isn't that difficult, just moreso relative to other ways of hosting a webapp.

When me and my wife were purchasing our first home, we had everyone from mortgage people to realtors to our own friends and family that owned homes tell us to never use our home insurance unless it was something truly catastrophic.

Currently our home insurance deductible is $10k. The logic is that anything less than that we can pay ourselves. Home insurance is only there to cover either catastrophic damage or really, a total loss.

What does the financial compensation need to be for an engineer to actually do this? I'm gonna assume that if you work at Apple and are being recruited by OpenAI, you are not a dummy. Then you probably know that doing something like this runs the risk of you getting sued by a trillion dollar company.

If I had a potential employer ask me to do this, I would reply "oh hell fucking no", withdraw my application, and notify my companies security, legal and HR teams.

But then again it's easy to have the moral high ground when you're not staring down an offer that will completely change your and your families lives. I'm sure most employees probably thought what I'm thinking until they are looking at a 7 figure offer.

I see this case pretty simply. The states want to prove that Meta knew what it was doing to kids and did it anyways to raise engagement. Meanwhile it looks like Meta is trying to sidestep that argument entirely by stating that social media addiction is not a formally recognized diagnosis, essentially saying that while it was slimey, it was not illegal.

Morally I side more with the states but legally you can't ignore the argument that Meta is making. I feel like if social media addiction does become a formal diagnosis in the future then Meta is screwed unless they drastically modify their product. But I also feel like the best time for that to have happened was in the 2010's when all this stuff started to ramp up, if it didn't happen then it's not going to happen now.