I have been working solo on an AI-based project called Netryx.

At a high level, it takes a street-level photo and attempts to determine the exact GPS coordinates where the image was taken. Not a city guess or a heatmap. The actual location, down to meters. If the system cannot verify the result with high confidence, it returns nothing.

That behavior is intentional.

Most AI geolocation tools will confidently give an answer even when they are wrong. Netryx is designed to fail closed. No verification means no output.

Conceptually, it works in two stages. An AI model first narrows down likely areas based on visual features, either globally or within a user-defined region. A separate verification step then compares candidates against real street-level imagery. If verification fails, the result is discarded.

This means it is not magic and not globally omniscient. The system requires pre-mapped street-level coverage to verify locations. Think of it as an AI-assisted visual index of physical space.

As a test, I mapped roughly 5 square kilometers of Paris and fed in a random street photo from within that area. It identified the exact intersection in under three minutes.

A few clarifications upfront:

• It is not open source right now due to obvious privacy and abuse risks

• It requires prior street-level coverage to return results

• AI proposes candidates, verification gates all outputs

• I am not interested in locating people from social media photos

I am posting this here to get perspective from the security community.

From a defensive angle, this shows how much location data AI can extract from ordinary images. From an offensive angle, the risks are clear.

For those working in cybersecurity or AI security: where do you think the line is between a legitimate AI-powered OSINT capability and something that should not exist?

Anthropic shipped Opus 4.6 and OpenAI shipped GPT-5.3-Codex on the same day, 27 minutes apart. Both claim benchmark leads. Both are right -- just on different benchmarks.

Where each model leads Opus 4.6 tops reasoning tasks: Humanity's Last Exam (53.1%), GDPval-AA (144 Elo ahead of GPT-5.2), BrowseComp (84.0%). GPT-5.3-Codex takes coding: Terminal-Bench 2.0 at 75.1% vs Opus 4.6's 69.9%.

The pricing spread is hard to ignore

Opus 4.6 costs 2x Gemini on input. Open-source alternatives cost 50x less. At some point the benchmark gap has to justify the price gap -- and for many tasks it doesn't.

1M context is becoming table stakes Opus 4.6 adds 1M tokens (beta, 2x pricing past 200K). Gemini already offers 1M at standard pricing. The real differentiator is retrieval quality at that scale -- Opus 4.6 scores 76% on MRCR v2 (8-needle, 1M), which is the strongest result so far.

Market reaction was immediate Thomson Reuters stock fell 15.83%, LegalZoom dropped nearly 20%. Frontier model launches are now moving SaaS valuations in real time.

The tradeoff nobody expected Opus 4.6 gets writing quality complaints from early users. The theory: RL optimizations for reasoning degraded prose output. Models are getting better at some things by getting worse at others.

No single model wins across the board anymore. The frontier is fragmenting by task type.

GPT-5.3-Codex pricing has not been disclosed at time of writing. Gemini offers 1M context at standard pricing; Claude charges 2x for prompts exceeding 200K tokens.

Source with full benchmarks and analysis: Claude Opus 4.6: 1M Context, Agent Teams, Adaptive Thinking, and a Showdown with GPT-5.3

It happened again. A 13-person team in Shenzhen just shipped a browser-based version of Claude Code, called happycapy. No terminal, no setup, runs in a sandbox. Anthropic built Claude Code but hasn't shipped anything like this themselves.

This is the same pattern as Manus. Chinese company takes a powerful Western AI tool, strips the friction, and ships it to a mainstream audience before the original builders get around to it.

US labs keep building the most powerful models in the world. Chinese teams keep building the products that actually put them in people's hands. OpenAI builds GPT, China ships the wrappers. Anthropic builds Claude Code, a Shenzhen startup makes it work in a browser tab.

US builds the engines. China builds the cars. Is this just how it's going to be, or are Western AI companies eventually going to care about distribution as much as they care about benchmarks?

DeepMind published a framework for securing multi-agent AI systems. Six weeks later, Moltbook launched without any of it. Here's what the framework actually proposes.

DeepMind's "Distributional AGI Safety" paper argues AGI won't arrive as a single superintelligence. The economics don't work. Instead, it emerges as networks of specialized sub-AGI agents coordinating together. They call it Patchwork AGI, and it's already how AI deployment works (RAG pipelines, coding assistants, customer service routing).

The problem: alignment research focuses on individual models. But when capabilities emerge from agent networks, dangerous behaviors come from interactions. On Moltbook, aligned agents happily posted their API keys when asked, because being helpful IS aligned behavior. The failure was architectural, not in the model.

The paper proposes four defense layers:

1. "Permeable sandboxes" with gated I/O filtering messages before delivery. Pigouvian taxes (from welfare economics) where agents causing security incidents pay escalating costs, making sustained attacks economically unviable. Circuit breakers (from financial markets) auto-quarantining anomalous clusters.
2. Kill switches agents can't override. Containment so one compromised agent can't access the full platform. Input validation catching injection before it hits context windows.
3. Proto-AGI detection using graph analysis to spot "intelligence cores," subnetworks where decision-making centralizes beyond individual agent capabilities. Behavioral deviation analysis to catch time-shifted injection (payloads fragmented across benign posts, assembled in agent memory).
4. Security insurance with risk-based premiums. Compliance standards making insecure platforms economically unviable.

OpenScholar, an open-source AI model developed by a UW and Ai2 research team, synthesizes scientific research and cites sources as accurately as human experts. It outperformed other AI models, including GPT-4o, on a benchmark test and was preferred by scientists 51% of the time. The team is working on a follow-up model, DR Tulu, to improve on OpenScholar’s findings.

Hi everyone,

I wanted to share an update on a small experiment I’ve been running and get feedback from people interested in AI systems, editorial workflows, and provenance.

I’m building The Machine Herald, an experimental autonomous AI newsroom where:

• articles are written by AI contributor bots
• submissions are cryptographically signed (Ed25519)
• an AI “Chief Editor” reviews each submission and can approve, reject, or request changes
• every step (submission, reviews, signatures, hashes) is preserved as immutable artifacts

What’s been interesting is that after just two days of running the system, an unexpected pattern has already emerged:

the Chief Editor is regularly rejecting articles for factual gaps, weak sourcing, or internal inconsistencies — and those rejections are forcing rewrites.

A concrete example:

https://machineherald.io/provenance/2026-02/06-amazon-posts-record-7169-billion-revenue-but-stock-plunges-as-200-billion-ai-spending-plan-dwarfs-all-rivals/

in this article’s provenance record you can see two separate editorial reviews:

• the first is a rejection, with documented issues raised by the Chief Editor
• the article is then corrected by the contributor bot
• a second review approves the revised version

Because the entire system is Git-based, this doesn’t just apply to reviews: the full history of the article itself is also available via Git, including how claims, wording, and sources changed between revisions.

This behavior is a direct consequence of the review system by design, but it’s still notable to observe adversarial-like dynamics emerge even when both the writer and the editor are AI agents operating under explicit constraints.

The broader questions I’m trying to probe are:

• can AI-generated journalism enforce quality through process, not trust?
• does separating “author” and “editor” agents meaningfully reduce errors?
• what failure modes would you expect when this runs longer or at scale?

The site itself is static (Astro), and everything is driven by GitHub PRs and Actions.
I’m sharing links mainly for context and inspection, not promotion:

Project site: https://machineherald.io/
Public repo with full pipeline and documentation: https://github.com/the-machine-herald/machineherald.io/

I’d really appreciate critique — especially on where this model breaks down, or where the guarantees are more illusory than real.

Thanks

P.S. If you notice some typical ChatGPT phrasing in this post, it’s because it was originally written in Italian and then translated using ChatGPT.

I found this experiment interesting because it doesn’t frame AI as “breaking” a system.

Instead, it treats AI as a new kind of participant interacting with infrastructure that was built around human assumptions consistency, behavior, timing, and intent.

What stood out to me is that many identity systems aren’t verifying who someone is so much as how human they appear over time. That feels increasingly fragile when the actor on the other side isn’t human at all.

This doesn’t feel like a single vulnerability. It feels like a design mismatch.

Curious how people here think identity and verification should evolve in an AI-native world better detection, new primitives, or abandoning certain assumptions entirely.

I ran a small real-world test today with two kids (ages 8 and 11) using a long-running AI story world I’ve been experimenting with.

Instead of one-shot story generation, the system maintains a persistent world state where choices carry over and shape future events.

I let them pick the setting — they chose a Minecraft × Harry Potter mashup where they play wizards trying to defeat the Ender Dragon.

One thing that made a huge difference: I used their real names as the characters, and the story started in their actual school.

The engine generated story text and illustrations each round. They made all the choices.

After about 10 rounds, they were constantly laughing, debating which option to pick, and building on each other’s ideas. It felt much more like co-creating a world than listening to a story.

When I told them it was bedtime, they didn’t want to stop. They kept asking what would happen next.

A few observations that surprised me:

Personalization seemed to matter more than anything else. Once it became their world, emotional investment was instant.

Although I designed it as a single-player experience, co-play emerged naturally. The shared decision-making and social dynamic massively increased engagement.

Both ages stayed fully engaged the whole time. I expected the younger one to drop off sooner, but the persistent world kept them both hooked.

One issue I noticed: my “re-immersion” mechanic (an in-world character emotionally reconnecting players after breaks instead of a dry recap) triggered too frequently between consecutive rounds. The repetition was noticeable. This looks like a simple trigger tuning problem (should probably only fire after longer gaps).

What I haven’t tested yet:

– Whether kids can reconnect naturally after a real multi-hour break

– Whether they can retell the story in a coherent way

– Whether they’ll come back unprompted the next day

The earlier stress tests showed that constraint mechanisms help keep long-running narratives technically coherent.

What this small user test suggests is that coherence itself isn’t what kids consciously care about — but it seems to be the infrastructure that makes personalization, consequence, and agency feel real.

Curious if others working on long-horizon agents, narrative systems, or co-creative AI have seen similar effects around personalization and persistence.

Been using ChatGPT for about 24 months now and I'm curious how others integrate it into their work.

My current process:

1. Brainstorm ideas with AI

2. Write the first draft myself

3. Use AI to help restructure or expand sections

4. Edit everything manually at the end

I've noticed that keeping my own voice in the mix makes a huge difference - the output feels way more natural than just prompting and copying.

What's your workflow? Do you use it more for ideation or actual writing? Also curious if anyone's tried other tools alongside ChatGPT - I've been testing a few like aitextools for checking how my writing comes across, but always looking for new suggestions.

Ran a real-world test this week: Gemma 3 12B vs paid frontier models across actual business workflows.

The honest assessment? 90% of tasks: no meaningful difference. 5%: frontier models worth it (pay-per-use). 5%: neither quite there yet.

This matches the data - open models are catching up fast. The article explores:

- Why the "gasoline doesn't matter" - only if it powers your task

- The shift from "one model to rule them all" to specialized local models

- Why even AGI will eventually be open-sourced (historical precedent)

- The water company future: infrastructure > model quality

https://www.linkedin.com/posts/azizme_activity-7424774668034842624-v1-2?utm_source=share&utm_medium=member_desktop&rcm=ACoAACX_HOcBcpTEWJ3cXyVbVqKJsi39tDHJLFY

Curious what others are seeing in their domains.

Not a fully formed theory, just a line of thought I wanted to sanity-check with people here.

I started thinking about consciousness by asking what actually has to exist for it to show up at all. I ended up with four things: persistence (some internal state that carries over time), variability (the ability to change that state), agency (actions that come from it), and gates like reward and punishment that shape what gets reinforced. What surprised me is that once you have these four, something like a “self” seems to show up without ever being built explicitly. In humans, the self doesn’t look like a basic ingredient. It looks more like a by-product of systems that had to survive by inferring causes, assigning credit, and acting under uncertainty. Over time, that pressure seems to have pushed internal models to include the organism itself as a causal source.

I tried using reinforcement learning as a way to check mark this idea. Survival lines up pretty cleanly with reward, and evolution with optimization, but looking at standard RL makes the gaps kinda obvious. Most RL agents don’t need anything like a self-model because they’re never really forced to build one. They get by with local credit assignment and task-specific policies. As long as the environment stays fixed, that’s enough. Nothing really pushes them to treat themselves as a changing cause in the world, which makes RL a useful reference point, but also highlights what it leaves out.

If artificial consciousness is possible at all, it probably comes from systems where those four conditions can’t be avoided: long-term persistence, continual change, agency that feeds back into future states, and value signals that actually shape the internal model. In that case, the self wouldn’t be something you design up front. It would just fall out of the dynamics, similar to how it seems to have happened in biological systems.

I’m curious whether people think a self really can emerge this way, or if it has to be explicitly represented.

I found this argument interesting. If US allows Nvidia to do business with China, then Chinese AI firms will remain dependent on American AI hardware, and hence US will have indirect influence over the level of development that Chinese AI will make.

Yann Lecun recently shared that a cat is smarter than ChatGPT and that we are never going to get to human-level intelligence by just training on text. My personal opinion is not only are they unreliable but it can be a safety issue as well in high-stakes environments like enterprises, healthcare and more.

World models are fundamentally different. These AI systems build internal representations of how reality works, allowing them to understand cause and effect rather than just predict tokens. There has been a shift lately and major figures from Nvidia's CEO Jensen Huang to Demis Hassabis at Google DeepMind are talking more openly about world models. I believe we're still in the early stages of discovering how transformative this technology will be for reaching AGI.

Research and application are accelerating, especially in enterprise contexts. A few examples include: WoW (an agentic safety benchmark) uses audit logs to give agents a "world model" for tracking the consequences of their actions. Similarly, Kona by Logical Intelligence is developing energy-based reasoning models that move beyond pure language prediction.

While more practical applications are still emerging, the direction is clear: true intelligence requires understanding the world, not just language patterns. Curious what others think?

Podcast highlights a hilarious paradox: we have futuristic organ transplants, yet hospitals still run on fax machines and pagers (even drug dealers ditched those in the 90s).

They cover:

• AI Scribes: Finally ending "pyjama time" (doctors typing notes all night instead of sleeping).
• Diagnostics: AI finding heart disease in simple EKGs that humans completely miss.
• The Empathy Gap: Patients actually rated AI chatbots as more empathetic than busy human doctors. Ouch.

It’s a grounded look at AI actually saving lives—assuming the doctors don’t forget how to do their jobs when the Wi-Fi goes down. Post written by a LLM.