2026-01-29 01:00:19
2 INTERACTIVE WORLD SIMULATION
3.1 DATA COLLECTION VIA AGENT PLAY
3.2 TRAINING THE GENERATIVE DIFFUSION MODEL
4.1 AGENT TRAINING
4.2 GENERATIVE MODEL TRAINING
5.1 SIMULATION QUALITY
5.2 ABLATIONS
7 DISCUSSION, ACKNOWLEDGEMENTS AND REFERENCES
4.1 AGENT TRAINING
The agent model is trained using PPO (Schulman et al., 2017), with a simple CNN as the feature network, following Mnih et al. (2015). It is trained on CPU using the Stable Baselines 3 infrastructure (Raffin et al., 2021). The agent is provided with downscaled versions of the frame images and in-game map, each at resolution 160x120. The agent also has access to the last 32 actions it performed. The feature network computes a representation of size 512 for each image. PPO’s actor and critic are 2-layer MLP heads on top of a concatenation of the outputs of the image feature network and the sequence of past actions. We train the agent to play the game using the Vizdoom environment (Wydmuch et al., 2019). We run 8 games in parallel, each with a replay buffer size of 512, a discount factor γ = 0.99, and an entropy coefficient of 0.1. In each iteration, the network is trained using a batch size of 64 for 10 epochs, with a learning rate of 1e-4. We perform a total of 10M environment steps.
\
\ 4.2 GENERATIVE MODEL TRAINING
We train all simulation models from a pretrained checkpoint of Stable Diffusion 1.4, unfreezing all U-Net parameters. We use a batch size of 128 and a constant learning rate of 2e-5, with the Adafactor optimizer without weight decay (Shazeer & Stern, 2018) and gradient clipping of 1.0. We change the diffusion loss parameterization to be v-prediction (Salimans & Ho (2022a). The context frames condition is dropped with probability 0.1 to allow CFG during inference. We train using 128 TPU-v5e devices with data parallelization. Unless noted otherwise, all results in the paper are after 700,000 training steps. For noise augmentation (Section 3.2.1), we use a maximal noise level of 0.7, with 10 embedding buckets. We use a batch size of 2,048 for optimizing the latent decoder, other training parameters are identical to those of the denoiser. For training data, we use all trajectories played by the agent during RL training as well as evaluation data during training, unless mentioned otherwise. Overall we generate 900M frames for training. All image frames (during training, inference, and conditioning) are at a resolution of 320x240 padded to 320x256. We use a context length of 64 (i.e. the model is provided its own last 64 predictions as well as the last 64 actions).
:::info Authors:
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:::info This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license.
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2026-01-29 00:24:35
Why the United States and other countries treat some tokens one way and others another way can be puzzling. Crypto is never the same as yesterday, but the legal system leans on rules that were written long before this industry appeared. That old toolbox, whether we like it or not, is now shaping how tokens are created, how they are sold, and how teams talk about them.
\ These rules influence the whole atmosphere around launches, presales, and community expectations. And one name often sits in the middle of this discussion: the Howey Test. When it refers to crypto, we may also call it nobody-wants-their-token-to-be-a-security. Let’s see why.
Howey’s name comes from a 1946 U.S. Supreme Court decision, SEC v. W. J. Howey Co., which shaped how investment contracts are defined in American law. In that ruling, the court explained that when people contribute money to a shared project and expect profits from someone else’s effort, the setup should be treated as a security.
\ In the original dispute, buyers were purchasing citrus groves without any plans to farm them themselves. They relied on the company’s management to run everything, which is why the court concluded that passive profit schemes shouldn’t escape securities rules.
You see, securities rules aren’t exactly friendly or easy. Once something counts as a security, teams fall under the long rulebook from the Securities Act of 1933 (or its equivalent in other countries). That brings forms, disclosures, audits, and long waits, plus huge legal bills that can drain a small project. Most builders want to ship features, not juggle filings or burn cash, so they try to avoid that label as much as possible.
\ Over time, the Howey test settled into four elements: money invested, common enterprise, profit expectation, and dependence on promoters or third parties. When crypto arrived, regulators didn’t switch to a new standard. They took their time discovering cryptocurrencies, and when they did, they applied some old rules to this new industry.
The DAO case sits at the beginning of this story. This decentralized investment fund by Slock.it launched in 2016 on Ethereum. Buyers sent ETH to receive DAO tokens, which let them vote on different projects. Things grew too fast, then everything collapsed when an attacker exploited a flaw in its smart contract and drained a massive amount of ETH. The event panicked the industry and caught the attention of regulators who had never handled anything like it.
\ In July 2017, the US Securities and Exchange Commission (SEC) released its DAO Report, which said the DAO tokens were securities under the Howey Test. The reasoning focused on fundraising, central coordination by the creators, promotional efforts, and the expectation that token holders would profit from the team’s work. The DAO itself dissolved, but the report stayed, along with others. It became the template for examining ICOs, token launches, and early presales.
From 2018, another major case added more nuance. The company Ripple had been distributing its native token XRP in different ways, and the SEC sued them —considering that XRP was an unregistered security. In 2023, a U.S. court ruled that XRP sold directly to institutions counted as a security, while XRP sold to the public on exchanges or distributed in other ways did not, since holders in those settings could not assume Ripple was using their money for development. This result showed that a token’s legal status can shift depending on how it’s sold or promoted. \n
Laws aren’t as fast as crypto, but they eventually arrive. By 2025, in addition to the Howey Test, other crypto-related regulations were introduced, including the CLARITY Act in the United States. This is a proposed law that sets out categories for digital assets such as digital commodities, investment-contract assets (securities), and mature blockchain systems. It aims to reduce uncertainty by making it clearer which regulator oversees which type of token.
\ CLARITY could free many tokens from automatic securities analysis, yet the Howey Test will remain part of the legal landscape. Tokens structured around profit promises, centralized control, or fundraising models that resemble traditional investments would still meet the Howey criteria. Builders need to keep this in mind when shaping token design, marketing language, airdrops, and staking features.
\ It’s also important to consider that the same crypto network may host varied assets such as utility tokens, governance tokens, yield-bearing products, and fully regulated securities. In Obyte, the native token GBYTE couldn’t be considered a security under the Howey Test (it wasn’t even sold, but distributed). However, anyone can create their own customized tokens with any purpose and features, and they may or may not be considered securities.
For its part, CLARITY may narrow the number of cases that rely on Howey, but it doesn’t send the test into retirement. When we understand both rules, the whole landscape feels easier to navigate, quirks and all.
Featured Vector Image by vector4stock / Freepik
2026-01-29 00:02:55
How are you, hacker?
🪐 What’s happening in tech today, January 28, 2026?
The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, Yale University's Decision to Admit Women in 1969, The First Successful Internet Connection of a Mobile Phone in 2000, The Launch of Lego Bricks in 1958, and we present you with these top quality stories. From Claude Book: A Multi-Agent Framework for Writing Novels with Claude Code to What Really Determines the Speed of Your PyTorch Code?, let’s dive right in.
By @hackernoon-courses [ 4 Min read ] Build a blog that lasts. Learn how to treat your blog like a product, apply SEO + product thinking, and grow your audience in 2025. Read More.
By @darialittlefield [ 7 Min read ] When apps continue to surface large volumes of weak signals, users learn to do the heavy lifting themselves. Read More.
By @korovamode [ 4 Min read ] When AI reduces the cost of building automation itself, adoption accelerates as it expands. Read More.
By @vladsavinov [ 14 Min read ] Learn how to benchmark PyTorch and CUDA code correctly. A practical guide to measuring GPU performance using CUDA events. Read More.
By @thomashoussin [ 11 Min read ] Claude Book is an orchestrated writing system using Claude Code. It uses subagents for consistency checks and a perplexity gate with rewriting against AI-slope. Read More.
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We hope you enjoy this worth of free reading material. Feel free to forward this email to a nerdy friend who'll love you for it.See you on Planet Internet! With love, The HackerNoon Team ✌️
2026-01-29 00:00:06
Visibility can be a difficult thing to hack. And unfortunately, with the way the internet is set up today, writing quality content alone may not be enough to set you apart from the hundreds of millions vying for dwindling attention spans.
If you struggle with growing your readership, you’re in luck. We just might have a tip or two to help out.
\
If you’re going to stand out from the crowd, you need to treat your blog like a product.
Adopting product management principles can be the difference between disappearing under the radar and growing an enduring community around your work.
Here’s a 5-step process you can integrate into your workflow:
This starts with understanding WHY you write, which is important because it defines what success looks like to you. You also want to clearly define the problems your writing solves, for whom, and the unique value you offer. This will serve as your north star for future content-related decisions.
\
Start by creating 3-5 ideal reader personas. Give them a story, identify their needs, and all the ways your content might satisfy them. Next, go from your 3–5 ideals to one that best represents the majority of your target audience. Then use your imagination (and research) to flesh out the following details: demographics, pain points, behavior, and practical needs.
\
Once you’ve crafted a proto-persona, you then want to consider how this person might find your work and engage with it. Do they come to you via Google search results? Social media? Newsletters? Word of mouth? All of the above? Figuring this out will inform your content distribution strategy and, in some cases, even the tone of your work. It’ll help you understand where to focus your content marketing efforts so that you’re not going around beating dead horses.
\
It’s commendable to want to hit the ground running and transfer all your great, E-E-A-T-compliantideas from your mind to the page. But if you’re interested in growing a brand that stands the test of time, you want to do so intentionally. And nothing says intentional like a well-thought-out plan. So before you start writing, create a calendar that outlines all the subjects you’d like to address over a period of time. If you’re just starting out, try drawing up an 8-week calendar (break it up into smaller chunks if you need to).
\n Don’t worry, I won’t tell:
\
Think of this as the crossing i’s and dotting t’s step. After you’ve spent time on strategy to satisfy your proto-persona, you want to tighten the bolts to make sure the actual content, once published, can be discovered, resurfaced, and, most importantly, tied back to you. In light of this, you can think of optimization as a mix of discoverability and credibility. These twin forces keep your work working for you long after you hit publish.
\ Finally, there’s the AI-shaped elephant in the room to consider. It’s changing everything, and to be a successful blogger in 2025, you must write in such a way that AI bots know to crawl all over your articles.
Taking in all of this at once might seem a bit overwhelming. The good news is that you don’t have to.
\ The HackerNoon Blogging Course, with its self-paced structure, on-demand video lessons, practical tools and templates (yours to keep), exercises, and a community to learn with, allows you to digest all the resources you need to grow your reach and authority as a writer. And that’s just in one of eight modules curated by a stellar Editorial team responsible for publishing 150,000+ drafts from contributors all over the world.
\ Want to grow an online brand that thrives even in the age of AI?
\
:::tip Sign up for the HackerNoon Blogging Course today!
:::
\
2026-01-29 00:00:03
TalkNet-ASD is an audio-visual active speaker detection model that labels speaking faces and outputs JSON speaker tracks for real-world footage.
2026-01-28 20:45:04
You can now save the 30% app store fee and let your users pay you directly.
This seems like a no-brainer. 30% more profit for a simple change? That’s just free profit, right?
I don’t think it’s that simple, and for most companies, I think it’s a mistake.
To break this problem down, you need to figure out:
What we (always) care about on this site is which of these will make you more money?
You can break the initial transaction into these elements:
\ Now lets look at each of these elements to see who likely wins this.
\ Verdict: I think the app stores win here.
For the web-based payments to win, you need a really good checkout page.
Even then, I think you won’t be able to overcome the power of single-click checkout.
Cornell conducted a study where they concluded that single click checkout increased order frequency in e commerce by 43% and items purchased by 37%.
This is different than session conversion level, but these numbers are huge. There is a reason that Amazon does it by default, and that Stripe is building the same feature.
The team at RevCat wrote a great article testing web checkout vs app store.
They found that the web billing led to slightly less take-home. My suspicion is that this is driven by the smoother checkout experience.
Now lets look at the recurring side. You can break this into:
\ Verdict: I think the web wins out here, driven by the bottom-of-funnel tactics that you can use to win back users.
Tactics like this matter a lot. Read more here.
As I’ve written before, your ultimate enemy is the complexity tax.
You need to protect your ability to ship code quickly and drive results in the long term.
Apple/Anroid app stores give you a lot of out-of-the-box functionality that you’d need to recreate (i.e., pay engineers to recreate).
\
This is a lot of functionality that you’ll have to build or pay another vendor to manage.
Your cancellation toolbox is stronger outside of the app store
If you build this yourself, you have a lot more options. I’ve seen the “correct” deployment of these options lower churn 10-30%
You run a product that gets used across desktop and web, so you can actually consolidate this over time.
You acquire most of your users via paid ads, so you can get users to pay a step higher in your funnel, therefore increasing conversion and ROAS.
Before this, you’d need to get them to download an app, then pay.
I suspect that monetizing off the app stores will reduce your distribution in these app stores. I have no proof, but I think it’s going to happen either directly or indirectly.
Firstly, all of the marketplaces use ranking algorithms to determine what you see. Ebay, UberEats, DoorDash, FB marketplace, app stores, etc
All of these look at some combination of “what’s best for the user” + “what’s best for the business”.
I was on the team at UberEats that ranked the homepage, so I can say this from firsthand experience.
At UberEats, we explicitly say that % fee you pay Uber directly impacts your organic ranking. This would be direct impact.
The harder one to determine is the indirect impact.
All ranking algorithms are fed by signals from the product, and the algorithm basically determine their weight of importance.
Apple would almost certainly take “payment retention” as a sign of quality and use that to boost the ranking of apps that retain well, as they are seen as good for the user.
If you monetize off the app, Apple can’t get that signal, and this is effectively held against you.
How material is this? I don’t know. But I think it’s there.
As with most advice, you should care less about what is best overall and care more about what’s best for you.
For the average startup, I don’t think the complexity is worth it. You have a limited amount of time and focus.
Once that 30% is millions of dollars, and you can pay additional people to handle that complexity for you, then it can be worth it.
Good luck out there,
Dan
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