u/trueleo8

A really good talk about how the news around Mythos and the hype that followed did not show the reality of their claims. Out of 79 reported vulnerabilities - 14 of them were not really a bug and 15 were already fixed in the latest code, only 20 something worth looking at. In that 20 "real" bugs, numerous only worked under assumption of an already malicious actor being present or an untrusted device installed.

Another key thing that he talks about is leaky bots. "Anything it tells you, it will tell someone else" - same bugs ( public or non public ) being reported by different people can cause discomfort in terms of who gets the credit for it.

When i first heard about the Dimagi Naxal comment. I thought to myself that 56 Inch Chota Bandar is just salty after the whole protest debacle. But after hearing that they replaced their IT Cell leader too and the new person is suppose to take a more "moderate position" ( dunno what that really means ). Hmm that's interesting, I thought to myself. People have started Dimagi Naxal page and it's above 500k now I think. Many regular liberal people on the internet assumed that they are the "Dimagi Naxals" in this case.

I think the reality is a tiny bit different. They are very specific about this - Kiran Rijiju clarifies about this in X post as well. So who are the new dangerous minority now ?

It's the new age leftists ( who they will dub as maoists / communists ). This is really smart on their side because as I saw with my previous interactions on how much people are ready to jump and attack even the utterance on Marx, this makes us the perfect target.
You have to realize something, I am not claiming that the student leaders in jharkhand, delhi etc are communists. The BJP has figured out with the CJP protest interaction that none of their previously made tactics of disinformation will work when people are united by an Idea not a party or person. So now they are going to divide on the Idea itself by creating a scope ( dimagi naxal though intelligent have bad ideas ) and project anyone or any idea which seems inconvenient will be grouped with this new villanified leftists.

This could also mean that it will much much harder to dismantle such propaganda because the narratives and arguments will be complex. Like the narrative of Hindu-Muslim can be dismantled by giving out example of a normal Muslim person in your inner circle. But this new fight will become much harder because not only you will need to defend your goodwill agenda but you will also need to explain why that agenda is not socialist/communist. IT cell will be putting people in imaginary camps of "dangerous leftists" and the right wing will just gobble it up.

This is just a speculation but do keep an eye out.

Additional Thoughts -
- I don't think it's a new thing that they are doing, but i think it will be their main alternative way of spreading their ideology on Instagram, X, Reddit.
- This also has additional benefit of retraction. They can retract this label anytime and say that "We meant these kind of people not you"

I am one of the few peoples in software engineering who already were aware of the tech industry enshittification before AI trend even began. I worked my first job at a startup and the signs it beings a crazy market was visible from the start, people care for code velocity more than quality. I left my job and did not apply to any other workplace because I was burned out. Then the whole AI news froze the hiring everywhere. This pushed me towards working on my own low budget software consultancy rather than relying on market to be sane again. But me and my co-founders did not find it worth while after 6 months and had to pivot because of widespread AI psychosis hurting our margins and there is no money for innovation unless it can be turned into a SaaS product.

We pivoted into financial investment algorithms and research as means of making money for short term and have grown into a team of 5 people now. Each of us from a different background in science ( I do most of the code and they being from academic side of finance, mathematics and physics ) do theory and analysis of financial instruments.

We are subscribed to Ed since last year I think and regularly follow what he has to say. But our AI usage is not zero and here where our problem began. We are paying for our AI usage through OpenRouter which charges on a per million token basis, this is simply because we want zero data retention ( which is opt in smh ). Hence all frontier models are simply not feasible for us to pay for every month. Mostly sticking to open-weight models like deepseek v4 flash/pro and kimi k2.6 which are well under $3-4 / M.

We were using AI to fix obvious code paths, debugging functions in our main strategy and creating analysis on market data that we were gathering. Writing long python notebooks by hand is very time consuming and AIs were giving us quite a productivity boost. Their reasoning seemed quite fine for the tasks we have.

This is where I think it clouded my judgment. Because we were up against a deadline we never set up any pipeline or rule-book for checking bias. So whenever we hit a strange result, we came up with all sorts of explanations except the obvious one - that something was wrong in the code or in our assumptions about market data.

Six months into development, while I was manually refactoring parts of it, I fixed a tiny bit of code. It was in a simple data loader utility that pulled bars data from our database into the model. Before it included one extra minute of data (a <= where it should have been <), it introduces a look-ahead bias and we weren't aware of it for the past 5 months and considered it as a positive result and ran with it. This was catastrophic because all of our assumptions and work were invalidated in an instant. This loader was generated by an AI, which we copied into our program on day one. Although the code was reviewed when it was added, it's the conjunction of this code with rest of our system was not explored by anyone.

Although this may look like that the same mistake can be made by a human. It wouldn't have happened if it was being worked on without AI help. Our main codebase is really small. AI's have contextualized this part numerous times, every time I have asked it to generate a new analysis notebook or something, it goes and reads the same data loader code. It has reasoned about our code base directly when I asked it to find any obvious bugs. It loves dissecting the complicated paths but never picked up this simple error. Any human would have caught this way way sooner if they had some time debugging and reading the code thoroughly, and took time to debug and check the output.

Now that we are snapped out of this AI psychosis it is really clear it's not just about this one error. AI changes the way you think. The worse part is that the output it gives now works 90/100 times. But just because it "works" does not mean it's the right thing to use. It's worse than gambling, it's like winning every time you are betting but there is a guy puncturing holes in your back and draining your blood out to offer it to the devil.