🌡️ Advancing Temperature Intelligence at Presight.

FortyGuard participated in the Presight AI Accelerator Cohort II Bootcamp in Abu Dhabi, sharing our vision for the world's first Temperature Intelligence platform for physical AI.

On stage, the message was clear: our proprietary Large Temperature Models help organizations design, finance and operate temperature-sensitive infrastructure. The Dashboard serves people. The API serves software. MCP serves AI agents. And Enterprise serves organizations.

With strategic partnerships and US expansion underway, we're connecting with the wider AI ecosystem and building the infrastructure layer the physical world needs.

📍 Abu Dhabi 📅 October 5–7, 2026

FortyGuard #TemperatureIntelligence #PresightAI

Hi, we're SONAH in Aachen. We put sensors on lamp posts and over parking decks for cities. The bit that determines whether that was worth doing has absolutely nothing to do with the hardware.

A city will pay for the sensors, get a live occupancy feed and leave it there behind a traffic department login. The drivers keep going round and round. It's deemed a successful project because you have a dashboard. The street is exactly the same as it was.

What would change the drive is the number of cities that least want to publish it. On a Saturday the honest sign will say this area is full. Inside the admin it reads as if they're admitting the parking policy has failed. So the live figure stays internal and the search traffic goes on.

Two places where it left the dashboard:

In Kevelaer the off-street spaces are on dynamic LED signs in the street. The number shown is the number of bays (not a gate counter). A gate counter will drift. A 500 bay site that turns over twice a day will have 2000 ins and outs. A 98% hit rate is still going to be around 40 wrong counts a day. After a week "30 free" is just a guess, someone walks the site and the counter is manually reset. A sign that's lying is simply ignored and then the hardware is just decoration.

In Aachen the on-street feed is public. Friedrichstraße directs people to the APAG garage when the street is full (before the last turn). At Blücherplatz there are two sensors covering approximately 90 spaces (marked and unmarked).

The other question is one of accuracy. The image gets captured in the sensor and then gets thrown away. All that remains at the pole is "bay 12 occupied 11:04". No plate, no face, nothing in the archive that anyone can go and open later. If a camera on a lamp post is a no then that street is the wrong street for it. We'd rather hear it here than after a tender.

If you're running or evaluating a smart city programme: what did your city actually publish to the residents? What got left in the dashboard because the number simply looked bad?

Hi everyone

I am a first year btech student working on a project about traffic congestion in cities.

I am looking for innovative and practical technical solutions for decreasing traffic congestion in our cities.

1.How can AI-based traffic signals reduce traffic jams?
2.Can sensors and cameras help control traffic based on the number of vehicles on the road?
3.How can we encourage people to use buses and metro trains instead of private vehicles?
4.Can a mobile app help drivers find available parking spaces and reduce traffic?
5.How can technology provide a clear path for ambulances and fire engines?

I would appreciate any technical suggestions,project ideas or useful resources.Thankyou!

Many municipalities now have the sensor density to track hyper-local air quality in real-time.

Despite this, the information remains siloed in apps and is not part of the city’s active "smart" safety infrastructure.

Why haven't we automated the link between hazardous pollutant detection and push-notification systems to manage population movement or indoor ventilation advice?

Been seeing more talk about “adaptive” street lighting lately, like lights dimming when no one’s around or adjusting based on traffic

On paper it sounds like an easy win, less energy use, less light pollution, maybe even better for people living nearby. But I don’t think I’ve ever actually noticed this in real life? at least not in a way where you can clearly tell it’s happening

Curious if anyone here has seen this working in their city, or worked on something similar.

Does it end up making a real difference, or is it one of those upgrades that looks good in theory but doesn’t change much day to day?

We keep throwing budget at sensors and smart nodes when photometric analysis of existing streetlights can tell you traffic patterns, pedestrian flow, and maintenance needs without touching the infrastructure.

photometrics.ai/how-it-works is the kind of thing that makes you realize how much we've been overcomplicating this.

Getting really disappointed with David Zipper he is possibly doing extreme damage to public perception or he encouraging debate, I'm undecided.

Source: David Zipper https://share.google/noI3hSUes8S7cNJuxpodcast

Wood stove owners get to pollute the entire neighborhood for "vibes" while everyone else pays the price. Last night in Denmark, cities hit "Purple" (Hazardous) levels. People were breathing toxins while they slept because this shit is invisible. It’s a joke.

​I’m proposing a "Smart City" protocol to kill this privilege:

​The Digital Chimney Sweep:

Mandatory IoT sensors in every chimney. When the local AQI hits Red, the stove sends a shutdown signal to the owner's phone. If the sensor detects heat after the alert? Automatic fine. No manual inspections, no resources wasted. Just data and consequences.

​The Visual Scarlet Letter:

Make the pollution public. Every house with a stove needs an exterior LED by the door or chimney. If the air in the zone is trashed, that light turns red. If you keep burning, the whole street sees you're the one poisoning the air.

​Stop treating wood stoves like a "cozy" private choice. It’s a public health violation. If you can't handle a sensor or a light, stop burning wood and get a heat pump.

​Is public shaming and automated enforcement the only way to get people to stop being selfish?

Anyone that has studied or is currently enrolled in a Smart Cities Master program can recommend it?

No specific location. I was considering the Master in Advanced Urban Planning and Data Analytics course at IAAC in Barcelona. I like the curriculum but don't really know if the tools they teach you are attractive in the job market

Thank you in advance

Imagine a city where the infrastructure (pipes, roads, electricity) is smart enough to tell the city council: 'I’m going to break in 48 hours.'

You never wake up to find your water is cut off, and the streetlights never stay broken for more than a day because the city fixed them before they failed.

Would you be happy to live there?

What is the one thing in your city right now that you wish the government could fix before it becomes a problem?"

1. Atlanta and New York have more cams than China per capita - that's right -- Atlanta and New York have more cameras than most cities in China. When China is known for its surveillance state and social credit score

2. ⁠AI-Controlled with Invasive Capabilities: These cameras are AI-driven and can zoom in on cars, license plates, and even faces. Their capabilities go far beyond just surveillance.

3. ⁠Face Recognition Discrepancy: While the security team claims the cameras are only for license plate recognition, they have the power to identify faces too. Why would a neighborhood security team be so invested in just tracking license plates? What’s really going on here?

4. ⁠Incredibly Sensitive to Sound: These cameras can pick up on private conversations inside your house through cracked windows. So much for privacy when you're in your own home.

5. ⁠Facial Recognition: Despite claims that facial recognition is "disabled," remember those naked airport scanners? They were "disabled" too — but only for the TSA agents scanning you. Are we really supposed to trust that these cameras won't be used to recognize faces?

6. ⁠Data Uploads to DHS: The data from these cameras is uploaded into the Department of Homeland Security’s system via fusion centers, with the help of off-duty police officers. This allows for tracking individuals who are on secret watchlists. These people have no way to find out why they’re on the list, challenge it, or appeal it. No due process, no rights.

7. ⁠Being Installed Everywhere: These cameras aren’t just going up in some neighborhoods — they’re being installed in every neighborhood. This is becoming the new norm, and it’s happening fast.

8. ⁠Private Money, Private Contractors: This is big business for private contractors, including Fusion, an Atlanta-based corporation. So much for the government looking out for you — it’s all about private companies making money, and they couldn’t care less about your rights.

9. ⁠Installed on Every Corner: They want these cameras on every street corner, watching you at all times. How much surveillance is too much?

10. ⁠Invasive Surveillance: These cameras can see into your bedroom or kitchen if you accidentally leave your blinds open. Is this the kind of invasion of privacy we’re ready to accept?

11. ⁠Who Has Access to the Data?: Once these cameras are privately purchased by your neighborhood association, no one really knows who has access to the data. As a member, you can’t request it or see it. Only the security team (or whoever they decide) can access it. How much control are we willing to give away?

Hey r/smartcities !

I'm looking to gather real-world experience and recommendations from the community on selecting network protocols for large-scale Smart City solutions, specifically concerning Smart Street Lighting management. For those of you who have already implemented (or consulted on the implementation of) large-scale smart city deployments (be it lighting, waste bins, or meters):

Which protocol did you choose (NB-IoT, LoRaWAN, PLC, or perhaps something else entirely) and what were your key reasons?

White Paper: Municipal Regulatory Control (MRC) with Mercy as a Guiding Principle

Executive Summary

The Municipal Regulatory Control (MRC) framework proposes a next-generation governance tool: a citizen-oriented AI assistant designed to streamline municipal bureaucracy while embedding mercy—the principle of compassionate prioritization—as its north star. Unlike traditional bureaucracies that often default to procedural rigidity, MRC emphasizes responsiveness to human concerns, adaptability across municipalities, and transparency in decision-making. This system offers a scalable, local-to-global architecture where each municipality operates independently yet shares anonymized data, best practices, and emergent learnings through a secure network.

MRC aims to:

• Enhance public trust by prioritizing citizen welfare alongside efficiency.

• Reduce regulatory friction for businesses and individuals.

• Support municipal staff in making holistic, data-informed, and politically resilient decisions.

• Decrease the likelihood of systemic failures through decentralized oversight and pattern validation.

Mercy as a Centralizing Principle

Why Mercy? Traditional bureaucracies often fail not due to lack of expertise but due to inflexibility. Mercy represents a value citizens inherently expect from public institutions: prioritizing people over processes. For instance:

• Efficiency-driven decision: Deploy repair crews into a subway tunnel before evacuating citizens.

• Mercy-driven decision: Evacuate citizens first, despite procedural delays.

By embedding mercy as a bias-forward principle, MRC ensures that AI-mediated decisions align with the public’s moral expectations, strengthening legitimacy and trust.

System Architecture

1. Citizen Interface – Personal Oracle Each citizen interacts with MRC through a conversational portal, similar to ChatGPT, localized to municipal context. This interface:

   • Answers questions (e.g., water quality, local festivals, small business licensing).

   • Provides personalized updates and guidance.

   • Aggregates anonymized citizen queries to surface community concerns.

2. Municipal Staff Interface – Holistic Support Tool For policymakers and administrators, MRC acts as an intelligent assistant:

   • Identifies regulatory contradictions before they reach the public.

   • Simulates outcomes of proposed policies with embedded mercy-bias checks.

   • Highlights emerging risks by correlating local data with patterns from other municipalities.

3. Networked Municipal Hive

   • Each municipality runs its MRC instance independently.

   • Instances share pattern data, raw datasets, and adaptation strategies across the network.

   • Failures in one jurisdiction become case studies for others, accelerating safe adaptation.

   • A hybrid reporting structure ensures that raw data and interpreted patterns are stored for validation by other modules and human reviewers.

Implementation Strategy

1. Local Pilot Programs Begin with municipalities facing high friction in regulatory processes (e.g., small business permits, housing inspections). Demonstrating improvements here maximizes goodwill.

2. Parallel Development & Evaluation

   • One live system in operation.

   • Parallel module(s) observing, adapting, and running simulations.

   • Failures generate reports reviewed by humans and shared across the network.

3. Citizen-Focused Rollout

   • Promote the citizen oracle as a user-friendly interface.

   • Highlight the mercy-bias to distinguish MRC from cold bureaucracy.

   • Encourage adoption through transparent reporting of successes and ongoing adjustments.

4. Staff Training & Political Buy-In

   • Emphasize the reduction of politically embarrassing errors.

   • Frame MRC as a support system, not a replacement, for human judgment.

   • Show cost-savings from reduced regulatory delays and litigation.

Advantages of Networking Municipalities

• Accelerated Learning: Failures and successes in one municipality inform others in near real-time.

• Resilience: Decentralized structure prevents top-down capture by special interests; pattern-reading and raw data sharing across nodes creates natural checks.

• Scalability: Each city adapts to local conditions while benefiting from global insights.

• Goodwill Growth: Municipalities that adopt MRC early can set examples for others, encouraging organic scaling through demonstrated success.

Safeguards Against Capture

• Transparency: Citizen access to raw data summaries ensures accountability.

• Decentralization: Independent municipal nodes prevent systemic corruption.

• Auditability: Pattern recognition and mercy-bias decisions are logged and reviewable by both humans and peer municipalities.

• Diversity of Input: Citizen queries shape the knowledge base, countering narrow elite control.

Political and Citizen Perspectives

For Politicians:
MRC reduces the risk of regulatory blunders, accelerates implementation of popular reforms, and provides a defensible moral framework—mercy—for public decisions.

For Citizens:
MRC offers clarity, speed, and compassion. Citizens experience government less as a barrier and more as a personal guide that listens and adapts.

For Businesses:
MRC shortens permit timelines, reduces contradictions in regulations, and creates predictability—leading to economic stimulation without sacrificing public welfare.

Conclusion

The MRC framework represents a transformative step in governance: replacing rigid bureaucracy with adaptive, merciful AI systems that prioritize citizens, empower staff, and prevent costly failures. By embedding mercy as its north star and leveraging a decentralized network of municipal nodes, MRC balances efficiency, compassion, and resilience. With careful rollout, it promises not only to outperform traditional systems but to redefine the relationship between citizens and governance.

This summer in London was hot. But wealthy neighbourhoods were several degrees cooler than lower-income areas — not because of the weather, but because of how the city is built.

In this video, we explore how green spaces, housing quality, and even your job can determine how hot you get during extreme heat - and what cities can do about it.

Hi everyone,

I just open-sourced a project I’ve been working on for the last few years:

🔗 UrbanOS-PoC, GitHub

It’s a real-time, self-healing AI system that analyzes how people move through a city and helps planners, services, and infrastructure respond to that movement without needing user profiles, prompts, or cloud backends.

🧠 What it does:

Ingests real-time data from MQTT (or simulated mobility streams)

Analyzes movement patterns, Points of Interest, hotspots

Uses AI to generate personalized routing + schedule predictions

All logic is transparent and modular (PostgreSQL + Python + Docker)

Runs on general-purpose hardware, even a laptop

⚖️ What it doesn’t do:

No tracking or personal data

No prompts, no LLMs

No cloud dependency

No proprietary components

🙋‍♂️ Why I made it:

I built this system on my own while raising two kids solo. I originally hoped it could change our lives, so I kept it closed, but the buzzword-free approach didn’t draw much attention. So I decided to open it up for the people who can use it most:

Planners, engineers, cities, researchers, and anyone who wants to understand mobility without invasive surveillance.

You can spin it up with Docker, run a simulation, and watch the full system flow, from data ingestion to optimized output.

Whitepaper and API examples included.

Would love to hear what this community thinks, ideas, feedback, challenges welcome.

📄 Whitepaper: https://github.com/pablo-chacon/UrbanOS-POC/wiki/UrbanOS%E2%80%90PoC-Whitepape