r/AINewsMinute

For years, edge AI has promised intelligence everywhere. In practice, most "edge AI" still means sending data to the cloud, relying on large Linux systems, or requiring expensive accelerator hardware.

SuperESP changes that.

Built on Atome LM v2, SuperESP transforms a standard ESP32 into a tiny AI appliance capable of running twelve practical applications entirely offline.

No GPUs.

No subscriptions.

No datacenter.

Just a microcontroller that costs less than a cup of coffee.

Every claim is verifiable and tied to a script.

What SuperESP Actually Is

SuperESP is not another chatbot squeezed onto a microcontroller.

It is a collection of specialized ternary AI models designed to classify events, patterns, behaviors, and anomalies directly on the device.

The current release includes:

Agriculture monitoring

Voice commands

Motion recognition

Gesture detection

Sound event classification

Machine anomaly detection

Air quality analysis

Energy monitoring

Occupancy estimation

Wearable activity tracking

Water leak detection

Predictive maintenance

It comes also with :

+ ESP32 OS

+ Universal Installer

Check out everything :

https://github.com/TilelliLab/atome-lm

So I was trying to recreate an website that is made with GPT-Image 2, using battle mode, to get good results, as battle mode usually allocates an relatively good model to look good to benchmarkers or ai nerds, so somehow either arena.ai (LMarena) has access to fable 5 still or, this is renamed and is probably claude opus 4.8 max, BECAUSE it made such an good image to web i believe its fable 5, don't curse me out or smth am just shocked, even if its not fable 5 and prob opus 4.8 max or smth, It's still so good, the svg it made is crazy too.

Investor Weekly Report 061 | FF EAI Robotics Exceeds Expectations with 242 Units Delivered, Raises 2026 Target to 2,000

① FF EAI Robotics Achieves 105 June Sales, Shipments and Deliveries, Reaches 242 Units from March–June, Raises 2026 Target to 2,000 Units

② FF Robotics Becomes the Star of Automate and Heads to ISTE Live 2026

Full Script

For Weekly Report Issue #61, I’d like to start with two important bits of progress: FF EAI robots reached another record-high month of sales, shipments, and deliveries, and we are raising our full-year target once again. This is also an early result that the team and I are proud to share, nearly two months after I returned as FF Global CEO and began driving our five new transformations.  

In June, sales, shipments, and deliveries of FF EAI robots reached 105 units. From March through June, cumulative volume reached 242 units, exceeding our original target of 220 units.  

At the same time, we have decided to raise our full-year shipment target to 2,000 units. This marks the second increase to our full-year target, following our April adjustment from 1,000 units to 1,500 units.  

So why has FF’s robotics business continued to exceed expectations, and what gives us the confidence to keep raising our delivery targets?  

First, our five unique strengths are beginning to show. Our robot device capability, in particular, is already becoming a real competitive advantage. With the six major series of the Full-Form FF EAI Robot World now complete, our “one brain, multiple forms” all-star robot super group is demonstrating unique value. At the same time, our other four strengths — the EAI Brain, Data Factory, Industry Bridge, and ecosystem flywheel — are also being gradually unleashed.  

Second, our products and technologies continue to evolve. We are building a full-stack EAI solution built on “VLA + World Model,” centered on the EAI Brain, and supported by our developer platform and Data Factory. Our first-stage goal is to build the No. 1 EAI Brain and No. 1 foundation model for the EAI robotics education ecosystem in the U.S. and, eventually, globally.  

Third, our sales capability and vertical industry ecosystem are gaining market validation and strong recognition from partners, schools, and government stakeholders. I will share more progress on this front in next week’s weekly report.  

Fourth, our team and organizational capabilities continue to strengthen. With the ultimate return of the founding team, our “Human + AI Agent” organizational model has significantly improved both decision-making and execution efficiency.  

Another piece of good news this week came from Automate. As the largest robotics and automation trade show in North America, Automate gave FF a powerful stage for our first appearance at the event. The Full-Form FF EAI Robot World drew tremendous attention, becoming “the star of the show” and one of the most popular must-visit booths at this year’s show. It attracted large crowds, potential customers, and ecosystem partners, and received focused coverage from mainstream media, including FOX, ABC, NBC, NPR, and others. LA Weekly even described FF as trying to build the Apple of robotics.  

The strong momentum on the show floor showed that FF brought consumer-electronics-level excitement to a professional robotics trade show. To me, this points to three important signals:  

No.1, Physical AI is accelerating toward real-world applications and mass adoption.   

No.2, as a U.S.-based EAI robotics company, FF is leveraging its Three-in-One ecosystem strategy and five unique strengths to help move the industry beyond single-product competition and into a new stage defined by practical applications, ecosystem development, and scaled deployment.  

No.3, this is not only recognition of FF’s product power, but also recognition of FF’s role in accelerating the arrival of the EAI robotics era through disruptive Physical AI innovation.  

Next week, from June 29 to July 1, FF will also take part in ISTE Live 2026 in Orlando, the largest education technology conference in North America. We will use this opportunity to advance collaboration with K–12 schools, educational institutions, FF Par partners, developers, and ecosystem partners. On the B2B side, we will accelerate the adoption of EAI robotics education products and solutions across more schools, educational institutions, and FF Par partners. On the B2C side, we will continue to use family education as the first-stage entry point into the consumer robotics market. Our goal is to build an EAI education ecosystem for children, schools, and the future talent pipeline, and to open the door to the Physical AI world for children at an earlier age.  

On the capital side, this Friday, the company filed a new S-3 registration statement with the SEC in accordance with applicable rules. This is a renewal filing ahead of the expiration of the original S-3 registration statement, which became effective in June 2023. The purpose is to maintain financing flexibility during this critical phase of implementing our Three-in-One strategy. Over the past three years, the company has used the S-3 very prudently. Of the original $300 million registered amount, only approximately $28 million was actually issued. Going forward, the company will continue to practice “Stockholders First,” gradually reduce its reliance on convertible debt financing, and move toward a healthier financing structure centered on operating cash flow, mid- to long-term financial investors, and strategic investment.  

On the EAI EV side, the U.S. Patent and Trademark Office recently granted FF a patent for its AIHER range-extending hybrid transmission system. FF’s first-of-its-kind technology featuring a “range-extension-first, hybrid-assist” architecture will provide the industry with a more efficient powertrain solution. It preserves the refueling advantages of traditional internal combustion vehicles while delivering an electric-motor-driven experience and reducing mechanical complexity. This further strengthens FF’s technical barriers and competitive moat in the Physical AI era.  

That’s all for this week. See you next week!” 

The Otto Score enables classification directly on DRAM chips exclusively with bitwise operations and achieves without floating-point or MAC units during inference 99.0 % accuracy on MNIST as well as 58.7 % on CIFAR-10.

This scientific status report summarizes the central findings of the Forward-Prop research project as of June 2026. It focuses on the methodological foundations, the Key-Findings and the implications for hardware-near inference. All information is based on the experiments and architecture analyses documented in the paper.

The core innovation consists in that each ensemble member is calculated completely independently and in parallel on separate DRAM rows. This increases the classification accuracy with growing ensemble size, without the inference latency increasing. Modern DDR5 banks with over 65,000 rows theoretically allow several hundred members simultaneously.

The training uses float32-based SGD to enable continuous gradients. The exported model however uses exclusively integer weights and bit-level operations such as &, |, ~, XNOR and Popcount. No multiplications, no divisions and no floating-point arithmetic are necessary during the inference.

• Channel: Transformed input representations such as luminance or color opponents, which each generate NC_slice uint32 containers per image
• Encoding: Thermometer-like bit patterns (e. g. exp8, sig8), which transfer continuous values into popcount-like binary patterns
• Member: Independent classifier consisting of frozen random projection W0 and trained target matrix
• Ensemble: Collection of parallel members, whose scores are summed and lead via Argmax to the final prediction
• MAJ3: Bitwise Majority-of-3 compression of several uint32 containers to 32 bits per neuron – lossy, but robust to noise
• HiddenN (H): Number of MAJ3 neurons per member, which each provide one bit per class vote
• Bit-Mass: Total information capacity calculated as H × EN × 32 Bits

The fundamental challenge lies in the transition from the continuous number world to the discrete binary world of the DRAM operations. While multiplications and additions are inefficient, row-wise bitwise comparisons can be executed highly in parallel. This requires a suitable binary encoding of the input pixels.

In the case of MNIST, classification already succeeds with raw binarization of the pixels (threshold 128). The 32-bit containers directly represent the shape of the digits. Already after one pass 86 % accuracy is achieved; iterative correction passes increase this to over 97 %. No special thermometer encoding is necessary, since the data is inherently binary-like.

CIFAR-10 poses higher requirements. Tiny intensity differences (e. g. 127 vs. 128) lead in 8-bit representation to strongly deviating bit patterns and break the similarity measurement via XNOR and Popcount. Thermometer encoding provides a remedy here, by ensuring that neighboring intensity values also receive neighboring popcount values. This restores the continuity of the input data in the binary space.

The target matrix corrects the log-odds of the bits projected by MAJ3. W0 remains unchanged during training and serves as a fixed random projection. The only trained component is the target matrix. Various step-decay modes (cos-time, pow, cos-err, const) and a soft error target (target-err) control the iterative learning process.

Experimental results demonstrate that increasing the ensemble size from 7 to 17 members enables an accuracy gain of around 8 percentage points on MNIST – without measurable influence on the chip latency. The row budget of modern DRAMs allows scalings far beyond the previously tested configurations.

In summary, Otto Score positions itself as a promising approach for energy-efficient edge inference. The method utilizes the inherent parallelism of memory rows and completely avoids expensive arithmetic units. Open questions concern the scalability to more complex datasets, the precise hardware evaluation as well as the combination with further channel- and encoding-specific transformations. The report underscores the potential to drastically reduce inference costs through architectural adaptation to existing memory technologies.

(Word Count: approx. 980; References: Forward-Prop Research Project, internal status paper June 2026)

📎 Source 1: https://forward-prop.nhi1.de/papers/otto-score-summary-2026-06.html

I realized a few months ago ago that I hadn't tried to think through something on my own in a long time. Math homework I always used chatgpt, writing was chatgpt, explaining stuff I had to check chatgpt first.

My teacher talked about this thing called cognitive surrender this year. How before google maps people actually knew how to navigate their city. Now literally nobody does because you just never need to.

I'm not anti AI at all, but there's a difference between using a tool and just letting it do all your thinking for you. My writing got noticeably worse when I had to do it without AI. I couldn't do basic mental math anymore.

So I spent the last month building an app called Rusty. Basically just daily practice for the skills AI is replacing, mental math, verbal fluency, articulation. Only a few minutes a day, nothing crazy.

(Funny thing is just from testing the app constantly my mental math got way faster lol)

It's completely free btw. I'm 16 and broke so I'm not trying to scam anyone, just the stuff that costs me money to run is behind a paywall.

Has anyone else noticed a decrease in their mental skills? or is it just me?

Tiktok | Instagram

Everyone expected AI to be the biggest story at Cannes Lions 2026.

Instead, the campaigns that walked away with the biggest awards had something else in common:

👉 Deep human insights.
👉 Cultural understanding.
👉 Solving real-world problems.

Here are the Questions for you

1. Has AI made advertising more creative, or just more efficient?

2. Which campaign from the article stood out to you and why?

3. Do you think brands are shifting back towards cultural storytelling instead of performance marketing?

4. If you were on the Cannes jury, which campaign would deserve the Grand Prix?

5. As AI-generated content becomes easier, what will make a campaign truly memorable in the next decade?

🎙️** The best insights and arguments from this discussion may be featured in an upcoming episode of the Business Made Simple podcas**t