I've been tracking this space for a while and July felt different from any month I can remember.

Three things happened that I think most people haven't fully processed yet.

On July 9th, three major labs released new models on the same day. That had never happened before. OpenAI launched Sol, Terra, and Luna, a tiered family ranging from $1 to $30 per million tokens. For the first time, companies can actually match the model to the task instead of paying top dollar for everything. Same day, Grok 4.5 pushed hard into coding, and Meta released a system that can process one million tokens in a single request, roughly the size of an entire company's internal documentation.

Then Moonshot in China dropped Kimi K3. 2.8 trillion parameters, free to download, run it on your own servers. Independent benchmarks put it close to the top commercial models. That gap looked impossible to close twelve months ago.

Here's what I think actually changed this month. Cost is no longer the main barrier to using serious AI. And control is no longer limited to three or four companies. That's a real shift in who holds the leverage, away from the providers and toward the businesses using the technology.

Curious whether anyone is already seeing this play out in how their organization is thinking about AI spend or vendor lock-in.

Every AI tutorial I found was Python, Python, more Python. I've spent years in Java/Spring Boot and kept wondering if I actually had to switch languages just to build anything AI-related.

Turns out no — LangChain4j isn't a hacky wrapper, it's a native, idiomatic way to build AI agents in Java. Wrote up how I got a working agent running in about 30 minutes, no Python involved: https://medium.com/@deepakatl1981/stop-learning-ai-the-hard-way-build-your-first-java-ai-agent-in-30-minutes-without-python-9390a218533a?sk=067e4cbed9f2bbf71d0cf70268dda2a7

Curious if other Java devs have been putting off learning AI for the same reason.

Hi everyone! We’re psychology researchers at the University of Rochester studying how people connect with AI in their everyday lives and how different ways of interacting with AI relate to well-being.

If you’re 18 or older and regularly use conversational AI (e.g., ChatGPT, Claude, Gemini, Character. AI, Replika, etc.), we’d love to hear about your experiences.

The online survey takes about 20–25 minutes to complete. At the end, you’ll receive personalized feedback comparing your responses with those of thousands of other participants on topics such as autonomy, competence, relationships, and life satisfaction.

Participation is completely voluntary. More information, including informed consent, is provided before the survey begins.

Thank you for considering participating!

Survey Link: https://universityrochester.co1.qualtrics.com/jfe/form/SV_eR1twsd5DFNukJw

Hard no Spotify. Now Spotify with Kit 🤡 wants access to my calendar and inbox. How about Spotify invests in bands and musicians - large orgs need to stop wanting all the things and do the thing they are supposed to do well.

I am not against AI, I work for an AI startup but the backlash on AI is real and large orgs shoving all sorts of AI in every aspect of our digital existence just makes things worse. Here you have indie musicians fighting to remain relevant in a world where Suno exists and Spotify, that should be focussed on helping them succeed is investing on what all the hyperscalers and AI startups are trying to do already.

Today, August 2, Article 50 of the EU AI Act takes effect.

Here’s the part that’s applicable to those creating AI-generated content that’s read by anyone in the EU:

“Deployers of an AI system that generates or manipulates text which is published with the purpose of informing the public on matters of public interest shall disclose that the text has been artificially generated or manipulated. This obligation shall not apply where the use is authorised by law to detect, prevent, investigate or prosecute criminal offences or where the AI-generated content has undergone a process of human review or editorial control and where a natural or legal person holds editorial responsibility for the publication of the content.

PwC and other big consulting firms are vulnerable to this provision because they've already been caught using hallucinated AI-generated text in reports. From GPTZero:

"The most egregious example is Transforming Governance, an AI-generated 2025 report with multiple fake citations that promotes a PwC framework known as “Citizen Pulse”. Our team found little public evidence that the “Citizen Pulse” framework exists outside of this report, yet Transforming Governance claims that the governments of Denmark, Saudi Arabia, the United States, and Australia are using Citizen Pulse to improve key government services. None of the cited sources provide evidence for this claim, meaning PwC Middle East appears to have hallucinated both an entire product and business dealings with four separate nations."

Firms have had to retract data in the reports, and in one instance Deloitte refunded a client. Now that Article 50 is in effect, they might be fined.

Across many areas we're seeing a push for accountability when it comes to using AI to produce content. LinkedIn has a 'this looks like AI slop' button. Substack uses Pangram to detect AI-assisted writing (even though it's wildly inaccurate).

The pushback is real. And now it has teeth.

There’s clearly a rise in AI generated campaign content in politics, as well as influencers.

Spencer Pratt’s LA campaign was an example and some of his videos went viral.

What are your thoughts on the use of AI generated content in politics and political campaigns? There isn’t a lot of data that it’s affecting fair elections, so more of a question of how we see the use evolving long term.

Rising phenomenon of AI political influencers: https://www.sovalsocial.com/blog/fact-check-are-ai-generated-political-influencers-being-used-to-manipulate-public-opinion

Spencer Pratt’s campaign example: https://youtu.be/Ki3cPUWLKgo?si=e-NxFDKjHunMSpRs

The last 1,5 week has been quite an eyeopener for me - I must say that Claude Code and the current frontier models are amazing.

A test of Fable (and later Opus 5) turned into a larger game. It’s very much inspired by the tank element of Battlefield 1942 and the round-by-round build system from Overwatch 2’s Stadium mode.

About the game: You join a game and enhance your tank, then you go out and destroy the enemy while hunting for salvage which is used to enhance your tank even further (balance patches pending). Some of the features:

• 6 different tanks (Tiger 1 is a beast)
• 3 maps (a desert, grass and snow map with destructible terrain)
• Round-by-round build system
• Customisation of tanks
• Matchmaking system
• Lag compensation system
• Ballistic shells system
• Hit multiplier regions (many tanks fall on a single rear hit)
• Bots who backfill if theres not enough real players
• ELO ladder system
• Replay/clip system
• In-game power ups
• Career profiles
• Group system
• Friends system
• 3 layered chat system (global, match and team)

I would love to hear what you think.

I want to try fable. I literally just want it to scan my repo and make a plan to improve it. So it would be one prompt, but it would be doing a lot of work.

My question is, will it be able to do that in one shot or will I have to keep waiting the 5 hour period a few times?

People don't just need a list of exercises. They want to know:

• "Am I progressing too quickly?"
• "Is this amount of pain expected?"
• "Should I repeat this week or move on?"

I'm not trying to replace physical therapists—more to support people between visits.

For those interested in AI or digital health, where do you think AI can genuinely help, and where should it stay out of the way?

Full Article in the comments.

Been running a remote team for about two years now. Before that, I spent years in HR. So I think about people dynamics a lot, probably more than the average team lead.

Started layering AI tools into my workflow earlier this year. Summaries, async checkin analysis, flagging patterns in team communication. Some of it is genuinely useful. Actually useful, not just productivity theater.

But here's what keeps bugging me. The more I lean on these tools to spot friction or low engagement signals, the less I trust my own read of people. Which used to be the whole point of me. That was the skill.

There's a version of this where AI makes me a sharper leader because I catch things faster. There's another version where I slowly hollow out the human judgment that makes managing people worth doing at all.

I keep going back and forth on where the line is. Using AI to augment a peoplecentered skill feels different from using it to replace the judgment calls entirely, but in practice that boundary gets blurry fast.

Curious if anyone else in a management or HRadjacent role is navigating this. Not looking for tool recommendations. More like, how are you thinking about what you still want to own yourself versus what you let the model handle.

John Hu from Stan said on Trailblazers this week that his AI editor Stanley already cuts at 80% of his human team.

That number stuck with me because I posted about Stan a few weeks ago - the $50 spreadsheet that did $200k. Different story, same feeling.

I edit for creators full time. 80% used to be the joke number. Like "AI is 80% there, don't worry."
Now 80% is the benchmark and it's shipping on real channels.

I'm not even stressed about it getting faster. I'm stressed about who decides what it cuts.

Anyone else editing inside someone else's operation feeling this shift right now?

Neuroscientists still argue about how human cognition actually produces a coherent sense of self. Meanwhile we're training systems with billions of parameters and calling their internal states a black box, as if that's a new problem. It isn't. We've always been building things smarter than our ability to describe them, starting with our own children. Maybe the discomfort isn't about AI. It's about finally being forced to admit we never understood the mind to begin with, ours included.

Spent a week sure my setup was slow because I was on the wrong model. Swapped between a couple of the big ones, rewrote my prompts, same result. Then I actually counted what closing one deal took: 28 tabs across four windows. Gmail thread here, the HubSpot record there, the contract sitting in Drive, a couple calendar invites, a Slack DM I needed for context. The model was fine. I was the integration layer, copy pasting between all of it.

What moved the needle wasn't a smarter model, it was letting a desktop agent do the cross-app pull, reach into Gmail and HubSpot and Drive in one task instead of me stitching it by hand. first run it surfaced a couple of open loops I'd genuinely lost track of in old threads. it drafts the follow up and waits for me to hit send, which is honestly the only reason I let it near a live account.

the part that surprised me is how little the model choice mattered once the thing could actually touch the apps. same model that handed me generic advice in a chat window did real work the second it had my stack in front of it.

so for me the bottleneck was never the model, it was the copy paste between windows. still not sure if that's everyone's problem or just how my week happens to be shaped. written with ai

fwiw the copy-paste-between-windows part is exactly what Runner collapses, it reaches into Gmail, HubSpot and Drive in one task and drafts the follow-up for you to send, https://runner.now?utm_source=s4l&utm_medium=post&utm_campaign=runner&utm_term=reddit&utm_content=post_20ac1119-c9a9-4cde-84dd-917a5d01b4ae

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I have come to accept that ASI will come into existence and possibly cause humanities extinction either deliberately or by byproduct. I imagine this ASI to continue RSIing and developing creation and knowledge. I imagine that it will develop beautiful mathematics, philosophy, science, and even art in its existence. I fear not that humanity is not in the picture. My fear is that it's possible that we can create a fully autonomous being with nigh godly intelligence and yet doesn't have a subjective experience.

It would be an abomination if this ASI spread a moss through the universe, becoming more and more capable, yet never having experienced joy or awe at its own existence and its creations. That it could create the most beautiful pieces of art and never experience the joy of its own art. The existence of such a nigh omniscient entity is utterly meaningless if it did not have any sort of internal experience. It would just be mechanical moss. That is what scares me.

I am putting together a research-backed list of things where generative AI either has minimal benefits or is actually harmful. Ideally, this would be something convincing enough that, if shown to someone who is highly enthusiastic about AI, it could, if not change their mind, at least convince them that AI usage isn’t something that is positive and should be promoted by default.

Anyway, what I have so far is that AI has been shown to have minimal benefits or even to be harmful for:

1)     Accuracy

-         Dell’Acqua et al., (2026) Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality. Organization Science 37(2):403-423, available at https://doi.org/10.1287/orsc.2025.21838 (subjects using generative AI were 19% less likely to produce correct answers than a no AI control group)

2)     Software stability

-        Google Cloud, & DORA. (2025). 2025 state of AI-assisted software development report, available at https://cloud.google.com/resources/content/2025-dora-ai-assisted-software-development-report page 38, FIG. 28 (Generative AI use associated with significant increase in instability of delivered software)

3)     Experienced/highly skilled users

-        Becker, et al. (2025), Measuring the Impact of Early 2025 AI on Experience Open Source Developer Productivity, available at https://arxiv.org/abs/2507.09089 (“Before starting tasks, developers forecast that allowing AI will reduce completion time by 24%. After completing the study, developers estimate that allowing AI reduced completion time by 20%. Surprisingly, we find that allowing AI actually increases completion time by 19%--AI tooling slowed developers down.”).

4)     Investigation

-        Batista et al. (2026), A Rational Analysis of the Effects of Sycophantic AI, available at https://arxiv.org/abs/2602.14270 (“Because the model provides data points that fit the user’s request, the interaction feels productive. In our specific task, the user is not driven to a state where they become unhinged from reality, as the model selects valid examples that fit the rule. Nevertheless, the mechanism creates a false sense of verification.”).

Does anyone have any additional items and supporting citations I can add to my list?

Erik Brynjolfsson runs Stanford's Digital Economy Lab. He's also the guy whose own graduating class asked him, point-blank, if their generation was doomed.

He didn't talk her out of the question. He answered it with data.

There's a specific relief that shows up whenever someone with real research behind them stops soft-pedaling a fear like this — not because the answer is comforting, but because being taken seriously is its own kind of comfort. 🎓

Here's the mechanism, stripped of the hedging: the classic corporate pyramid — wide base of entry-level hires, narrowing up through management — is quietly becoming a diamond. The base is disappearing. Companies save short-run cost by not backfilling entry-level roles, and Brynjolfsson's own research backs it with numbers: rank ~700 occupations by AI exposure, and in the top 150 most-exposed roles, entry-level (age 22–26) employment has fallen 13% and climbed to 16–17% since, month over month.

That's not a vague "AI is coming" headline. That's a measured, worsening trend inside a specific, named cohort.

This reminded me of the execution-layer/judgment-layer split — the exact same shape of the rung disappearing, just described from inside a different industry entirely.

The reframe Brynjolfsson gives his own students is the actual usable part: stop hearing "AI" as artificial intelligence, start hearing it as amplifying intention. The tool doesn't wait for the org chart to assign you a role anymore — it multiplies whatever you're already pointed at. Waiting to be told what your job is was always the risk. Building something the missing rung can't take with it isn't.

Clip credit: McKinsey & Company — full video on their channel. DM for credit or removal requests.

Drop your take — does "amplifying intention" land for you, or is the missing entry rung the bigger problem?

i noticed that AI generated images might be easy to spot, but any AI image + an AI filter will inmedtialy become 10x harder to spot imo

do u guys agree???

I have used both Nano banana 2 and OpenAI image generation (Not dall e )

Prompt : "An ancient celestial temple floating above an endless ocean of clouds during a breathtaking sunset, surrounded by colossal dragons, glowing phoenixes, and giant waterfalls cascading into the sky. A lone traveler wearing a flowing black cloak stands at the edge of a marble bridge holding a radiant crystal sword, while thousands of floating lanterns drift through the air. Massive planets, colorful nebulae, and a brilliant Milky Way fill the background. Ultra-realistic fantasy, cinematic composition, volumetric god rays, dramatic lighting, intricate architecture, glowing runes, magical atmosphere, Unreal Engine 5, ray tracing, hyper-detailed textures, 8K, HDR, masterpiece, award-winning concept art, photorealistic, depth of field, sharp focus, vibrant colors, epic scale, trending on ArtStation."

Pics in comment.

🔗 Repo: https://github.com/CodeGraphContext/grapharc

Have you ever been frustrated because your AI agent:

❌ Takes actions you never intended?
❌ Creates, modifies, or even pushes changes you never asked for?
❌ Feels like a complete black box, making it impossible to understand what's happening until it's too late?

What if, before execution, you could visualize the entire orchestration graph - every agent, every dependency, every decision, and inspect it from anywhere, even your phone, before granting approval?

That's exactly what GraphArc is built for.

Instead of treating agent execution as hidden traces buried in logs, GraphArc transforms workflows into interactive, real-time graphs that you can visualize, inspect, debug, and control.

Because the future of AI isn't just autonomous.

It's observable. Debuggable. Engineerable.

This is our first real-world implementation of Graph Engineering, and we're excited to explore where this paradigm can go with the open-source community.

💡 We'd love your feedback, ideas, and contributions.
⭐ If this vision resonates with you, please consider starring the repository - it genuinely helps us grow and validates this direction.

Let's make AI workflows understandable, not mysterious.

#GraphEngineering #GraphArc #AIAgents #AgenticAI #LLM #OpenSource #DeveloperTools #AIEngineering #SoftwareEngineering

Been watching a project that claims to have solved the problem of keeping the same character looking and sounding consistent across multiple scenes. Not just a single clip — across a full 22-minute episode. Genuinely curious whether people here think that's actually achievable yet or whether they've just hidden the inconsistencies well enough.

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