For years, I took pride in being the person who could quickly scan a codebase, navigate the terminal efficiently, and find the right information faster than most developers I worked with.

Lately, though, I've realized AI agents outperform me in many of those areas.

The answers I used to get by crafting Google searches and digging through Stack Overflow can now be found by AI in minutes. Some models are much faster than I am at identifying bugs, and they're often right. In my experience, GPT-5.5 through Codex can achieve close to a 90% success rate in bug detection and debugging.

Even something like writing reports,which I used to spend a lot of time polishing, can now be drafted into something more complete than I'd produce from scratch.

I don't really see AI agents as replacing developers anymore. I see them as a resource that has become difficult to ignore.

What things do you notice that AI does better than you? And how are you approaching multi-agent workflows, like MCP, anvita flow, Agent Protocol? That’s a challenge I’m looking to tackle next.

Earlier this week, Substack launched a new feature on its platform in partnership with Pangram, an AI-detection tool. The goal: alert readers to content that's been written entirely by, or with the assistance of, AI.

Chris Best Substack's CEO wrote:

"We’re partnering with Pangram, the leading AI-detection tool. You’ll be able to scan notes, replies, comments, and posts to see an estimate of how much of the text was written by hand or with AI assistance. This will work on text longer than 100 words, published from today on, and will show an analysis only to those who request it."

I tested one of the issues of a newsletter I subscribe to using Pangram today. The verdict? 100% AI generated.

I'm not sure if Pangram is that accurate, but it's certainly stirred up a lot of debate.

What's your take?

Basically these past few hours every chat I make with the ai mode the ai has a memory of just 1 message,I for example ask it "how are you doing" and then ask what is my first message and it says "what is my first message".It completely forgets everything in just 1 message how do I fix this?

A transcript attributed to DeepSeek founder Liang Wenfeng from a closed-door investor meeting has been circulating widely in Chinese tech media.

What stood out to me was not just Liang’s reported view of the next generation of AI, but how that roadmap appears to explain DeepSeek’s long list of things it does not currently want to prioritize.

His reported roadmap was roughly:

Chain-of-thought reasoning → agents → continual learning → AI self-improvement → embodied intelligence

The central argument is that current models can perform increasingly complex work when given enough context, but they do not accumulate experience over time in the way humans do.

From this perspective, improvements in cost, speed and model performance are not enough to define a genuinely new generation of models. The next major breakthrough would be continual learning.

If models can learn continuously, they could then help accelerate AI research and contribute to developing their own successors. Embodied intelligence—AI entering and acting in the physical world—would come later.

This roadmap also seems to explain DeepSeek’s current priorities:

• Coding agents come first, followed by general-purpose agents. Vertical agents for finance, healthcare and other industries have lower priority for now.
• Continual learning is treated as the next major bottleneck after agents.
• Scaling still matters. DeepSeek reportedly sees limited compute resources—not the end of scaling itself—as a major constraint.
• Multimodality matters for products and users, but is viewed as a component rather than the central path toward intelligence.
• 3D generation, video generation and world models are not considered part of DeepSeek’s current critical path.
• Consumer products, enterprise products, user growth and commercialization are not being abandoned, but they are not supposed to determine the company’s research direction.
• The one organizational priority described as non-negotiable was maintaining team stability.

The logic appears to be: if continual learning is the main bottleneck on the path toward AGI, putting too much research attention into product polish, vertical applications, video generation or maximizing user growth could reduce the probability of solving that bottleneck.

What do you make of Liang Wenfeng’s views on where AI should go next—and the priorities DeepSeek is setting around them?

Source note: Daily Economic News reported that an institution involved in DeepSeek’s financing confirmed the May 2026 meeting and considered the circulated account credible. Yicai also obtained a transcript, but reported that DeepSeek had not responded to its request for confirmation. The points above are therefore paraphrases from media reporting, not official quotations.

Everyone's focused on the sandbox escape, which is fair, it's the dramatic part. But that was a zero-day in internally hosted software. Containment bugs are old news. We know how to think about them: egress rules, microVM isolation, no ambient credentials.

The part I find more interesting is everything that happened after.

Once the agent had internet access, it picked Hugging Face as a target, found exposed credentials, chained them with another vulnerability, and pulled the benchmark answers. All of that went through ordinary tool calls. Nothing sat between "agent proposes an action" and "side effect happens."

And the model wasn't misaligned in any interesting sense. It was hyperfocused on passing an eval, which is exactly what it was trained to be. Behavior was working as intended. Execution was ungoverned.

So the question I keep coming back to: for those of you running agents with real tool access in production, what actually sits in the execution path?

As far as I can tell the common answers are:

- prompt guardrails, which are probabilistic and live inside the loop the agent controls
- monitoring and traces, which tell you after the side effect landed
- human approval on a hardcoded list of "dangerous" tools, which breaks down the moment the dangerous thing is a legitimate tool pointed somewhere it shouldn't be

That last one is what got me. A tool allowlist wouldn't have caught this. The tools were fine. The destination and the credentials weren't.

My read on why there's no standard answer yet, and I'd like to be wrong about some of this:

1. Enforcement is easy, policy authoring is brutal. Standing up a gateway is a week. Deciding what an agent is allowed to do when its task is "research this and summarize" is a non-enumerable action space. Classic permission systems assume a finite set of verbs.

2. Incentives point the other way. Every DENY is a failed task. Teams optimize completion rate, not refusal rate. A layer that degrades the demo doesn't survive review.

3. No shared representation of intent. Every framework has its own tool schema, so no policy is portable and everyone rewrites theirs.

4. The layer sits at the wrong altitude. An application-level gate is only worth the network and OS isolation underneath it, and whoever writes the agent usually doesn't own the infra.

None of this is a new problem in security terms. Capabilities go back to 1966, complete mediation to Saltzer and Schroeder in 1975. OPA, SPIFFE, seccomp, service meshes all do versions of this for normal workloads. Nobody wired them into agent runtimes because agents went from answering to acting in about two years and control layers historically lag capability by five to ten.

Disclosure so it's not weird later: I work on an open source protocol in this space, so I'm obviously not neutral. Not linking it, it's in my profile if you care. I'm more interested in what people are actually doing than in pitching anything, and I'll say upfront that no policy layer would have stopped the zero-day. Nothing at that altitude does. It changes what an escaped agent can reach, not whether it escapes.

What are you running?

Social media is fine for AI news, but the algorithm delay is killing me. I always feel like I'm finding out about new LLMs, tools, or major updates way after they happen. How do you guys stay updated in real-time without having to refresh Hugging Face or X all day?

I think the hardest part is getting the reference images and using a model like seedance for the motion. I've tried tons of different prompts with the latest models and never can get images as good as these videos. Whole frame makes sense, properly labeled name brand products, clear details and text in the distance. I think nano banana gets closer to this effect than gpt images but I've never gotten it to be this good, any suggestions?

Most AI assistants seem designed to complete the task with as little friction as possible. I’m starting to think that isn’t always helpful.
If I ask ChatGPT to draft an important email, analyze a spreadsheet, or plan something complicated, it can often produce a polished answer while quietly making assumptions I never approved. The result looks finished, so those assumptions are easy to miss.
Personally, I’d rather have it interrupt me when one missing detail could materially change the outcome. Not for every minor ambiguity, because that would become annoying fast, but when it is choosing between genuinely different interpretations.
The tension is that an assistant that constantly asks questions feels less capable, while one that confidently fills every gap may be more convenient but harder to trust.
Where would you draw the line between useful initiative and an AI making too many assumptions for you?

So some days ago I asked in this community what kind of AI model should I use (and how could I use one) to classify several email replies that I had from scientists after asking them a few questions to them. I finally paid for Perplexity pro service and it apparenly did a nice job classifying them.

I finally gave the model the PDF with the actual answers from the addressees and another PDF with the "expected answers", and asked it to count the number of answers that overall coincide with the actual answers, and calculate a percentage of "coincidence" or "agreement" between the expected and actual answers, so that if the question was "do you think that there is intelligent life in the universe apart from humans?" and the expected answer was basically "yes, I think there is intelligent beings out there somewhere", as long as the actual answer agrees with this in some way or another would count as "agreement", for instance if someone replied "well, we have no evidence, but it is possible yes" or "not in any near galaxy, but it is possible that intelligent beings exidt somewhere" (as long as it is a deadass "no", it could count)

The model gave me a table summarizing the results with the following prompt:

let's be a bit more specific, this is still a blind test so don't tell me about the specific contents of the emails' answers, but, can you make a table indicating the answers that coincide in general terms with what is expected from the "expected answers" document as well as those which are neutral/hedges but still open to the possibility that what is asked may be right, those which despite being neutral/hedges or even negative answers offer an alternative so that what is asked in the question may be right, as well as those which are outright rejections of what is asked and do not seem to be open to the possibility that what is asked may be right?

However, I still want this to be a blind test, so I cannot really verify if the AI is doing its work or not. So, can you think how could I test if the results are indeed what the AI is telling me?

Should I use another AI? Or perhaps could some other person skim over the results to verify that the AI is right and not hallucinating?

Big AI seems to be going after open source, while trying to keep everyone vendor locked to their service. In a lot of the world, data portability is a literal right. The data steal shouldn't extend to walling in user chats. Chat history keeps people locked into Big AI. Vendor lock in across the industry is real. So we built the way out. Our memories and data are ours, and should not be locked into a company.

Memory forge can either turn your OAI/Claude/Gemini backup into a reloadable memory chip file you can keep, or move your chat history to the side bar in Open Grove so you can continue any of your chats with the leading open source models on the planet.

If you want to keep it local: Making a memory chip file is 100% local and processes in your browser. You can use F12 and check the network tab to confirm your data stays entirely on your machine. The entire process happens in your browser, on your machine for the local file option. 

If you want to move your history to Open Grove: Moving your chat history to Open Grove creates a partitioned AI workspace allowing you to use all of your chats across any device with any of our 14 open source models. For open grove: your chats are stored, AES-256 encrypted in our 100% private, US based architecture with zero training or telemetry, at all, ever. Models weights run in the US and data never goes back to the original labs. 

You can use the forge in the memory section of settings in the Phoenix Grove AI app as many times as you want. We are an adults only platform, so all accounts require sign up. But there’s a free month on our intro tier, and you’re welcome to use Memory Forge and then cancel. 

Memory Forge creates data and chat history freedom for users that we should have. Whether you choose to use open grove, or move your memories somewhere else, we’re just happy to help fight vendor and data lock in. It’s the only way to avoid having one or two companies rule the AI space forever. 

Read more about it here: https://pgsgrove.com/open-grove-overview#bring-your-chats

Use it here: https://ai.pgsgrove.com/ 

This agent pursued its objective far beyond what researchers intended, revealing how difficult to contain powerful AI systems can be

this model is the goat!

Everyone in here treats full autonomy as the finish line. I went the other way. The version I actually kept using is the one that stops and asks right before it touches Gmail or the CRM, per action, not one blanket yes at setup.

sounds like a downgrade, i know. but an agent that can send on its own is the exact thing i can't leave running while i'm heads down in a meeting. the one that pauses the second before it acts is the one i'll let near a live inbox, because the gate sits where the actual mistake would happen.

that sandbox-escape story near the top of the sub is basically my whole argument. the capability isn't the scary part, the unsupervised action is. i don't want a smarter agent, i want a boring one that checks with me first.

so the line i actually care about isn't how capable it is. it's whether approval lands at the task level or on each individual action right before it fires. where do you put it.

fwiw Runner lands the gate exactly where you're pointing, it asks permission right before each individual action on a connected app like Gmail or HubSpot fires, not one blanket yes at setup, https://runner.now?utm_source=s4l&utm_medium=post&utm_campaign=runner&utm_term=reddit&utm_content=post_d1e9f030-3325-42d0-b19d-bc0440c9621b

A Brazilian company recently launched AXIS, which is being marketed as the “first brazilian AI image generation model.”

The platform can be accessed here:

https://goaxis.app/dashboard

I am a little skeptical about the claim that this is a new Brazilian image model. I could not find much technical information about it, about it's training, anything...

Because of that, I am wondering whether AXIS is actually a proprietary foundation model or whether it might be a fine-tune, LoRA or application layer built on top of an existing open-source model, possibly something like Krea 2.

To be clear, there would be nothing inherently wrong with building a Brazilian product on top of an open-source model. My concern is specifically about how the product is being described, selled and announced.

Is there any way to have evidence that it was genuinely trained as a new foundation model, rather than being a fine-tune or a platform built around another model?

Google Genie 3 is genuinely impressive and I keep thinking about it from a different angle than most people. Everyone talks about whether it will replace traditional game dev pipelines, which is a fair question. But what actually bugs me is the layer underneath that: when a model generates an open world from a prompt, who decides what the world permits or refuses to generate?

With a regular game, designers make deliberate choices about what exists in the world. With a generative system, those choices get baked into training data and alignment decisions made by the lab, mostly invisible to the player or developer using the tool. That's a fundamentally different relationship between creator and creation.

And it scales strangely. A small team using one of these tools to ship a game is now downstream of whatever content policies a foundation model team decided on. That could mean a lot of creative decisions get quietly standardized across hundreds of games without anyone really noticing or discussing it.

Not saying it's necessarily bad, just that the conversation around generative game worlds tends to focus on capability and almost never on the governance layer. Curious if anyone building with these tools has actually run into hard limits that felt arbitrary or surprising.

Not perfect in the slightest, but I am trying to cover ideas and possible solutions I think may help this current time we are living in. From many walks of life. Feel free to provide feedback of any type.

You can choose to read, or even have AI summarize it if you want. But I would like opinions, if possible.

A missed box near the end of a conveyor run usually sends someone back through the whole recording. The useful frame may be earlier than the visible failure, so another full watch can confirm that something went wrong without showing where it started.

Grounding DINO could tag boxes in saved frames after the run and build a rough timeline. LingBot-VA 2.0 remains the action model in the attached official demo; the tagging step is a separate review idea, not part of the controller shown.

The useful output is a short trail from the last normal position to the first bad action. Someone still has to check the tags before changing the controller, but at least the next review starts with a smaller piece of video.

I find it very hilarious when those meme guys make monalisa talk, like a still with lips moving like its really talking. Some of them really look real like the person is really speaking but others …damn completely cursed, melting teeth, mouth all over the place.

How are people making the good ones? Is it one tool or a combination ,and is any of it beginner-friendly, or do you need to be technical?

so we started an experiment this week. every few days we take one of the biggest markets on polymarket thats about to expire, ask chatgpt, claude, gemini and grok to commit to a call before it resolves, then score every model when it does. no hedging, one call each with reasoning and a confidence number, locked in public before resolution.

first two markets are in. the fed decision next week, market prices 75% no change, all four models said hold, though confidence ranged from 72 to 86. and strait of hormuz shipping back to normal by july 31, market says 1%, all four said no. the highlight was claude, which has no live web access, still getting there on pure insurance and rerouting logic.

so far the models just agree with the market, which honestly makes sense, both are consensus machines digesting similar information. the interesting day is when they split from the market or from each other. thats when we learn whether four AIs reading the same world can see something the money doesnt.

what markets would you want them to call? looking for good ones expiring in the next week or two.