Digital Dopamine

Digital Dopamine

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Tune in for a weekly dose of digital dopamine! Explore productivity apps, uncover tech trends, and dive into short coding tutorials tailored for new developers. Subscribe for insights that supercharge your tech journey! digitaldopaminellc.substack.com

5d ago

Local Remotion Animations W/ Ollama, OpenCode, and Gemma4

Local Model Aspirations For all of the folks out there who want to embrace AI but also have issues with the energy consumption and cost, I’m right there with you. When AI started to become mainstream and then eventually mandatory in enterprise work, I was very reluctant to use it and train these models that my company will eventually replace me with. Well…….that happened verbatim 😅. My previous place of work has been going through rounds of layoffs after a new set of C-suite execs strolled in to make “improvements” to the business, aka, “the company isn’t making enough money so, hire offshore, fire employees with IP knowledge, and go heavy on using AI.” I’m obviously exaggerating (kinda), but the point I’m trying to make is that AI is great when you have a solid plan on how to use it and try not to rush the process. Most companies didn’t take that approach, though, and got shot in the foot because of it. Does that mean we should oppose AI? Absolutely not. I actually love what AI can be used for, and I think there isn’t enough fine-tuning out there for specific use cases. Now, with Chinese models becoming much more competitive in intelligence, the cost difference is a no-brainer, and many companies are switching to these cheaper yet very capable models. (Image Source: https://techstartups.com/2026/06/29/western-companies-are-quietly-switching-to-chinese-ai-models-as-u-s-frontier-ai-prices-rise/) Then, when you use platforms like DeepInfra as your provider, scaling becomes less of an issue, and you only pay for usage and not a monthly subscription. Add the OpenRouter API in the mix, and you have the ability to swap providers at will. If costs ever change, if one provider hits you with rate limits, or if a provider goes down completely, OpenRouter is the failsafe that will forward your request to a different provider automatically (with the proper configurations) so your clients won’t have issues with the products/software you built for them. This just goes to show how far we’ve come with AI development, and like it or not, it’s a tool almost as important as the internet itself but potentially much more dangerous. While that danger may include physical harm, I’m more worried about the mental and intellectual harm that it’s doing and will continue to do if we don’t make some major changes in how the average person should approach AI. This is why I started diving into local models, with the understanding that they are not as capable as the frontier models, but that’s the point. They are SLMs and MLMs that you can leverage, but it will still require you to do some sort of critical thinking, which is a good thing. There is absolutely no reason for people who are using AI to just chat or ask a simple question, to be using the compute power and energy consumption of the frontier models. That’s one reason why I marveled over Google’s Gemma 4 series because, in theory, they have a model that should serve on just about any device. In practice, though, I’m learning some major limitations with these models, but most of them have to do with local compute power (as well as my current lack of knowledge on how to fine-tune these systems). That isn’t to say local models will never work; we have come so far with local models already that I'm sure in the next year or so, there will be a better generation of local 1st models that will work as smoothly as using a frontier cloud model. And “smoothly” is the keyword here, because in terms of intelligence, that’s not the main selling point of these local and more ethical models. Local Model Experimentation So that leads us to this lil ol’ experiment with me trying to build a “lower third” animation for my podcast intro. A “lower third” is just an animation on the lower left or right of your screen with the host name and title, or whatever you’d like to pop up there briefly (or at least that’s how I understand it 😄). So you’d think that a local model would be able to knock this out no problem, right?? If you have a beefy enough local machine, you’re golden. However, most people do not have a $5k+ home lab that can run these local models without major lag and bottlenecks. When the Gemma4:12b model was released, I was ecstatic because it was small enough for my Mac Mini (M4) not to hit memory limits, and it allowed enough headroom for me to record on OBS without OBS crashing. But reality hit me hard when I needed this model to run efficiently in any capacity for the task at hand. So I crafted 3 separate prompts for this task since, given the small context window limit of 32k I had to set, we needed to make sure to break up the tasks into individual prompts and sessions so the 12B model can stay as focused as possible. The original prompts are below: [1/3] Create src/LowerThird.tsx. Transparent background. Static layout only, no animation yet: a 12px-tall horizontal gradient bar (linear-gradient 90deg, #7B5BF0 to #4B7BE5) at x=120 y=880, an off-white (#F4F4F6) rounded rectangle behind two lines of text: primaryText in Archivo bold 48px #111111, secondaryText in Space Grotesk 32px below it. Props: primaryText, secondaryText (zod schema). Register in src/Root.tsx: id "LowerThird", 1920x1080, 30fps, 150 frames. [2/3] Animate the entrance (frames 0-21): the gradient bar scales in from the left using spring({frame, fps, config: {damping: 200}}), the rectangle then expands from behind it, the two text lines slide up 20px and fade in with a 4-frame stagger. Use only useCurrentFrame/interpolate/spring. [3/3] Animate the exit: frames 129-150 reverse the entrance. Hold frames 21-129. Then I'll run pnpm lint and paste any errors. But these evolved greatly throughout my multiple rounds. Before we get into what the final prompts were, we need to make sure that folks who want to follow along can get set up with the tools I used during this process. Setup 0. Scaffold the Remotion project # Prereqs: Node 18+ and pnpm pnpm create video # → name the project, pick a template (Blank is fine — the agent writes the rest) cd pnpm dev Then install the skills: npx skills add remotion-dev/skills That drops remotion-best-practices into the agent folders (.opencode/skills/, .claude/skills/, .agents/skills/, …) and records it in skills-lock.json, so OpenCode — and any other agent you point at the repo — picks it up automatically. 1. Serve the model with a real context window Ollama’s default context (~4k tokens) is far too small for agentic coding — OpenCode stuffs system prompt + skill rules + file contents into context, and when it overflows, Ollama silently truncates the oldest tokens. The model “forgets” your instructions mid-task and the failure looks like model stupidity. It’s a config problem. Note for context We will be setting 2 different contexts for Gemma. One will allow me to record at the same time but will reduce my context window. The other will give a better context window for a lesser chance of hallucination. You will also need to create Modfiles for our customized models to reference. Each modfile will be above the cat call. You can name the files whatever you want, but I’ve named the models according to their context window. To bake it in permanently via a Modelfile: Smaller Context cat > /tmp/gemma4-12b-16k.Modelfile Larger Context cat > /tmp/gemma4-12b-32k.Modelfile 16k is the sweet spot on 24 GB: roughly 7.6 GB weights + a few GB of KV cache ≈ 11–12 GB, leaving headroom for Remotion Studio (Chrome) and OBS. 32k works too if you’re not recording at the same time. You can also serve whatever model you have with a bigger context by running it with that flag. # Quit the Ollama menu-bar app first, then serve with a bigger window: OLLAMA_CONTEXT_LENGTH=16384 ollama serve But it’s better to set a standard so that you don’t have to serve a bigger context window with each launch of the model. 2. Point OpenCode at Ollama Create opencode.json in the project root (~/code/remotion_animations): { "$schema": "https://opencode.ai/config.json", "model": "ollama/gemma4:12b-16k", "agent": { "build": { "temperature": 0.1 } }, "provider": { "ollama": { "npm": "@ai-sdk/openai-compatible", "options": { "baseURL": "http://localhost:11434/v1" }, "models": { "gemma4:12b-16k": { "name": "Gemma 4 12B (16k)" }, "gemma4:12b-32k": { "name": "Gemma 4 12B (32k)" } } } } } Three notes: * Model ids must match ollama list exactly — these are the two Modelfile variants built in step 1. If the id doesn’t match a local tag, OpenCode’s requests just 404 against Ollama. * Top-level "model" sets the default, so OpenCode launches straight into the 16k local model — no /models dance every session. Swap to the 32k variant with /models when you’re not recording. * "agent": { "build": { "temperature": 0.1 } } turns the sampling temperature way down for the build agent. For codegen you want boring and deterministic, not creative — a 12B at default temperature wanders; at 0.1 it follows the skill rules much more reliably. You can also just make this update in the OpenCode root opencode.json file, which would typically be located at /Users/{user-name}/.config/opencode/opencode.json. So in my case, it would be /Users/kdleo/.config/opencode/opencode.json 3. Launch and verify cd ~/code/remotion_animations opencode LowerThird Build Rounds I found out quickly that name collisions stall on 12B. At least it did for me. Even though I had a different path listed for the creation, because the actual file name was the same as what I created during my dry run with a different model, it kept telling itself it needed to check that file, but then went back to saying “wait, the path is code/user/file-name.txt, I should just create a new file at the path specified,” but then it would go back to thinking it
Jul 9

Hack With Me Ep.4 | Windows, CLI, and Networking

Ever wondered what actually happens when you type a URL into your browser? In this episode, I break down the fundamentals of networking — from the OSI model to raw TCP connections — no prior experience required. 📌 What we cover: - The OSI network model - IP addresses, subnets, and routing - TCP vs UDP and port numbers - Connecting to a TCP port from the CLI Whether you're studying for a cert, starting a dev/sysadmin role, or just curious how the internet works under the hood, this one's for you. If this helped, consider subscribing to learn with me 🤘🏾 — new episodes drop regularly. 🔗 Connect: 🦋 Bluesky → https://bsky.app/profile/digitaldopamine.dev 📸 Instagram →https://www.instagram.com/digital_dopamine_llc/ 💻 GitHub → https://github.com/kdleonard93 👾 Discord → https://discord.com/users/407639833146818570 Get full access to Digital Dopamine at digitaldopaminellc.substack.com/subscribe
Jun 15

The Quiet Deal That's Reshaping Wall Street | Digital Dopamine Ep. 9

I Thought We Had More Time.... It feels like not too long ago, when I published my article and 5th episode of the Digital Dopamine podcast, “Tokenizing Private Credit, Real Estate, National Debt, and Everything Else“, where I warned about institutional efforts t start tokenizing assets and little did I know, some major players were relatively low key but making move swiftly. The biggest players are The DTCC and Stellar Foundation, who inked a deal to enable the tokenization of DTC-custodied assets on Stellar’s blockchain. This news in itself is actually pretty cool, in my opinion. My bias stems from knowing Stellar’s past and how far they’ve come since the inception and initial release in 2014. They set out to be one of the few blockchains that wanted to bridge the gap between traditional finance and exchange by making products that allowed people who had family in underdeveloped nations to supply their family members funds almost instantly with little to no fees as well as no major worry about exchange rate. This of course sounds amazing and you’d think everyone would be on board with this adaptation but, as you can probably tell, the Society for Worldwide Interbank Financial Telecom (SWIFT), would not be a fan of losing it’s grip on international exchanges. SWIFT is a Belgium based cooperative that is essentially owned by the banks and other member firms that use its service. Which further supports evidence that the banking system fights so hard to stonewall blockchain based companies in order to make their own similar product to then move their customers on before they lose them to new and leaner companies working natively in blockchain. Now some might say that this isn’t really a “bank vs blockchain” situation and more so a question of if SWIFT can do what Stellar and XRP does, but in house. That’s the thing though, if SWIFT wanted to do it in house, why wouldn’t they try to stall regulations? You can’t convince me otherwise that the massive opposition and lobbying around regulatory frameworks for crypto isn’t influenced by big banks trying to keep money flowing through their pipelines. Link to article -> https://www.ccn.com/education/crypto/xrp-xlm-face-challenge-50-banks-back-swift-new-payments-framework/ I digress, though; The point of this article is to address the rapid adoption of tokenization. The deal the DTCC did with Stellar isn’t the first and probably won’t be the last deal it has done with Layer 1 and Layer 2 blockchains. Before Stellar, DTCC partnered with Digital Asset on the Canton Network to tokenize DTC-custodied U.S. Treasury securities, with DTCC serving as co-chair of the Canton Foundation alongside Euroclear. That MVP targeted the first half of 2026, and in July 2025 a broad industry group already completed live 24/7 trades with onchain intraday and after-hours financing using tokenized Treasuries on Canton. Separately, DTCC named Chainlink as the data and orchestration layer for its forthcoming tokenized collateral platform — another distinct partnership worth a mentioning. DTCC also has a concrete platform timeline independent of any single chain: limited production trades begin in July 2026 with a full platform launch in October, covering Russell 1000 stocks, ETFs, and Treasuries under an SEC no-action letter obtained in December 2025. So the DTCC is not new to this, they true to this. But they aren’t the only ones in the race to tokenized assets. You have ICE, the Intercontinental Exchange, not the useless and corrupt Immigration and Customs Enforcement agency. The Intercontinental Exchange is the owner of the NYSE and they are backing tokenized securities initiatives tied to OKX . Then you have the Nasdaq developing infrastructure for blockchain based stocks with Kraken parent company Payward. There are plenty more examples of big players making moves but the problem is that no one knows about it! I can go up to almost any one of my family or friends, intelligent folks, and 90% of them would not know that these institutions and depositories are trying to tokenize let alone know what the tokenization of assets even is. So we need more education around this subject and hopefully folks can start to pay attention to who’s making deals with who around these major changes, so that you can plan accordingly to either shuffle your assets around, learn more about blockchain and crypto to maybe invest in blockchain companies doing REAL and PROVEN work to improve our financial systems and railways, or just so that you understand it and not surprised when you see weird changes in your retirement account in terms of allocations and processes. Wait, We DO Have More Time.... With these changes, it doesn’t mean we don’t still have time to learn a bit about them and about the companies behind them. So I’m gonna focus on the 2 main players of this article, Stellar and the DTCC. Stellar (XLM) Stellar was launched in 2014 by Jed McCaleb and Joyce Kim. McCaleb was a notable figure in early crypto history — he had previously created the Mt. Gox bitcoin exchange and co-founded Ripple (XRP) before a falling out with Ripple’s other co-founders led him to start a new project. Stellar’s codebase initially forked from Ripple’s protocol, but the team rewrote the consensus mechanism, resulting in the Stellar Consensus Protocol (SCP) — a federated Byzantine agreement system designed to be more open and decentralized than Ripple’s approach. Unlike many crypto projects with a profit-driven corporate structure, Stellar is steered by the Stellar Development Foundation (SDF), a non-profit. You can probably see why I actually respect this blockchain company and strongly believe they deserve a role in our financial systems future. From the outset, its stated mission was explicitly aimed at financial inclusion and cross-border payments — connecting “people, payment systems, and banks” and serving populations underserved by traditional banking. Between it’s inception in 2014 to 2018, Stellar was making major moves inking partnerships and implementing their protocol overhaul. The amount of real world business deals made were a clear sign to me that this company will be insanely successful, and they were! Now I wasn’t invested in crypto during this time since I was still very new to the space. I was young and broke as well, so investing wasn’t the first thing I did with the bit of extra cash I had (even though I should have 😭). But from 2014-2018, Stellar exploded +7000%........... yeah i know, I’m salty as about it too. Even today, as of 6/13/026, its up 5,990% since it’s inception. The era of legit blockchain companies going to the moon like this is long gone. But that’s not to say this is no longer a good company to invest in. If anything, it might be an even better time since you wont have to worry about it being a really risky bet anymore. It’s intertwined in so many partnerships with new and legacy business alike, from a partnership with IBM to launch “World Wire,” a cross-border payments network built on Stellar’s rails, involving a number of banks experimenting with stablecoin-based settlement, to the expansion of anchors (regulated on/off ramps) across Latin America, Africa, and Southeast Asia, where remittance use cases gained the most traction. Even more with stablecoins, Stellar has become one of the better networks for stablecoin issuance due to its low transaction fees and quick settlement times. USDC, a stablecoin owned by Circle (ticker: CRCL), is a great example if a stablecoin issued via Stellar, given the fact that they are a pretty big public company now. And again, that’s just some of the partnerships and use cases it had BEFORE 2024. In 2024 they then released their next huge update, which was the launch of Soroban. Soroban was Stellar’s smart contract platform, in a way similar to how Ethereum has smart contracts. That’s about the only thing Stellar and Ethereum’s smart contracts have in common, the fact that they are smart contracts lol. Ethereum smart contracts run on the Ethereum Virtual Machine (EVM), with contracts built primarily in Solidity. Stellar’s Soroban takes a different approach: it uses the WebAssembly (WASM) runtime, supporting a broader range of languages, with Rust being the primary one. Rust’s appeal here is partly about performance and memory-safety guarantees that reduce certain classes of bugs common in smart contract exploits. Soroban’s advantage is that it was designed from scratch to avoid the cost/scalability problems Ethereum is still managing, while inheriting Stellar’s existing strengths in payments, remittances, and institutional tokenization credibility via the DTCC deal. Below are a few more facts about Stellar and it’s impact: * Stellar ranks fourth among blockchain networks by tokenized real-world asset (RWA) value, with roughly $1.4 billion in RWAs and about a 5.24% market share of that segment (per RWA.xyz data as of mid-2026). * Its core technical advantages — low transaction costs, fast settlement, and a consensus mechanism not reliant on energy-intensive mining — have made it a popular choice for remittance corridors, stablecoin issuance, and now institutional tokenization pilots. * The network operates as a public, permissionless blockchain, but with built-in compliance tooling (such as configurable asset controls) that make it attractive to regulated entities — a middle ground between fully open chains like Ethereum and fully permissioned ones like Canton. The DTCC (Depository Trust & Clearing Corporation) The DTCC was formed in 1999 through the merger of two organizations: the Depository Trust Company (DTC), founded in 1973, and the National Securities Clearing Corporation (NSCC), founded in 1976. Both were created in response to the “paperwork crisis” of the late 1960s and early 1970s, when Wall Street’s back offices were overwhelmed b
May 31

Obsidian Obliterates Opps

Ohh, Where Have You Been, Obsidian… Very rocky life over the last month and a half, and I was kinda in a funk due to my recent layoff. I wasn’t able to avoid the fade this round 😭 Fuggin’ AI…. But I’m looking at this as a new chapter in my life to force my hand in exploring new opportunities while also being able to spend more time on Digital Dopamine. That being said, I realized that a lot of the organizational tools I used stemmed from tools I used at work. Most of these tools, though, were paid for by the company, and outside of my workday, I didn’t really need any personal organizational tools outside of generic Apple apps. When time started to feel like it was slipping away from me and being wasted on procrastination, bad timing, or just flat-out forgetting. Made me realize how much I need clear direction and organization to be productive, so I decided to do some hunting for some new and free tools I can use for my personal station. I went through a lot of options, and none of them seemed to be as geared towards developers as Obsidian was. Plenty of single-use cases within the other software, but Obsidian takes the cake because of its maturity and plugin ecosystem. I was looking for an all-in-one solution, and with the right plugins, Obsidian fulfils all of my (current) needs. That being said, Joplin and LocArk were close runner-ups, and depending on what you’re looking for, those options may serve you better. There was another open source product called Memos, and it’s a self-hosted note-taking timeline. It resembles Bluesky or a Facebook feed, but it’s just quick, timestamped notes. The devs put it nicely: “A self-hosted timeline for quick notes, daily logs, links, and snippets. Open it, write in Markdown, and move on.” So I plan to use this in tandem with Obsidian. Leaning In Given the state of AI, I have no choice but to lean in. And I’m not talking about using it here or and there or using Cursor as my IDE from now on, but really REALLY lean in. I’m talking certs, deeper research into the history and academic papers, Databricks proctored exam, projects, etc. In this world of capitalism, we have to adapt or get left behind. Not because we aren’t still capable of good work, but because company shareholders have no ethics and require consistent profits year in and year out. So if you work for a public company, know that you most likely will need to adapt to using AI if it hasn’t already been shoved down your throat. This is one of the main reasons I started using Obsidian. I needed a new tool that I can use to keep myself organized and on pace to get done what I need to get done. Calendar events and notifications can only do so much, and I started to find that the tools that I usually used with work or even my personal projects all started to limit the features behind subscriptions, and became more of pay-to-use software than having a pretty generous free tier for individual devs. Notion was one of my go-to apps for dev notes and runbooks, and while the free tier is still pretty generous, the tool as a whole is so bulky with other things that can’t be used without a subscription, and AI is also something that isn’t too subtle in the app anymore. So the hunt began, what was free that had the same capabilities as Notion and didn’t force you to have a subscription to use FREE PLUGINS…..😒 (a bit salty) The thing is, what Notion offers for free can be obtained by other products that aren’t as cluttered with all of the premium features Notion has to offer. Through my search, time and time again, I saw Obsidian as the top contender IF not the winner, of the best software to use as a knowledge base and notes app for devs. I could not have been happier with my discovery. Whatcha Got, Obsidian? Now that we know how much I have geeked out over Obsidian, let’s actually take a peek at just a bit of what it has to offer. The sheer scope of the plugin library makes this software such an extensible tool, and there’s no way I can (over ever will lol) cover all of them. We will cover some of the more important plugins, though the main focus is on Obsidian and what the tool includes by default. The Vault You Didn’t Know You Needed To me, the feature that stands out the most, since Notion was lacking it, is the vault concept (seems more common than not these days). Everything lives in a folder on your machine. Plain .md files. That’s it. No proprietary format, no data stored on AWS or Google Cloud servers. If Obsidian disappeared tomorrow, you’d still have all your notes, fully readable in any text editor or app that reads markdown files. That peace of mind alone is worth switching, and on top of that, this makes Obsidian the perfect tool for introducing AI agents into your knowledge base and note-taking process. You can have multiple vaults too — so if you want a clean separation between, say, your job search grind and your personal dev projects, that’s a two-second setup. Keeping It in Sync One question I immediately had after setting up my vault was, “Okay, how would I be able to sync up Obsidian with the mobile app without using Obsidian’s paid sync option?” Since we’re on Apple hardware, the answer was shockingly simple — iCloud Drive. In Obsidian on your Mac, when you create your vault, navigate to your iCloud Drive folder as the save location. Obsidian will create its own folder there automatically. Then, on your iPhone, download the free Obsidian app from the App Store, tap Create new vault, and toggle Store in iCloud on. From there, open the vault switcher, and your vault shows up ready to go. One thing to note — make sure your vault lives inside the Obsidian folder that the app creates in iCloud Drive, not a manually created folder. iCloud needs that app-generated folder to handle sync correctly. You can verify this in the Files app under Browse → iCloud Drive → Obsidian. Setup Docs → https://obsidian.md/help/sync-notes Markdown First, Always Everything in Obsidian is Markdown. If you’re a developer and you don’t already write in Markdown, you’re gonna learn to love it real fast. Headers, code blocks, callouts, tables, and checkboxes — it all renders beautifully in preview mode. And because it’s just .md files under the hood, your notes are version-control-friendly. Throw that vault in a Git repo, and now you’ve got note history. You’re welcome. # 1. cd into the vault folder cd /users/{my_username}/Documents/Obsdian # 2. git init git add -A git commit -m “Initial Commit” # 3 gh repo create project-name --public --source=. --remote=origin --push ⚠️ Heads up — that command above uses the GitHub CLI (gh). If you haven’t installed it yet, the push step will fail. You can grab it with Homebrew: brew install gh gh auth login Run gh auth login once follow the prompts, you’re good to go from that point on. Backlinks & Internal Linking — This Is Where It Gets Good This is the feature that will most likely be the catalyst for how my notes and doc creation evolve. In Obsidian, you can link any note to any other note using double brackets — [[like this]]. That seems simple enough, but the magic is the backlinks panel. Every note shows you a list of every other note that links to it, automatically. No manual cross-referencing. You start building a web of connected knowledge without a need for heavy configuration or reliance on plugins. The Graph View Okay, I’ll be honest — the graph view is 30% useful and 70% just deeply satisfying to look at. It renders a visual map of all your notes and how they connect to each other. As your vault grows, this thing becomes this sprawling network of nodes, and it genuinely feels like you’re looking at your own brain. Useful for spotting orphaned notes (stuff you wrote and never connected to anything) and identifying your knowledge clusters. Canvas Canvas is Obsidian’s infinite whiteboard feature, and it’s built right in. You can drag notes, images, cards, and web links onto a free-form board and arrange them however your brain needs them arranged. I’ve been using it for mapping out my AI learning path — laying out topics, drawing connections between concepts, dropping in reference notes. Think Miro or FigJam, but local-first and totally free. Daily Notes There’s a core plugin called Daily Notes (more on core plugins later) that creates a new dated note every day with a template you define. This is where my Obsidian and Memos workflow starts to come together — Memos handles my quick, throwaway timestamped thoughts throughout the day, and Daily Notes is where I do my actual structured reflection, task tracking, and progress logging. Two different tools, two different use cases, zero overlap. Templates Speaking of templates — Obsidian has a built-in templating system that lets you define reusable note structures. Spin up a new note and insert a template with a hotkey. I plan on creating more templates to become more efficient and consistent with my content depth and length. Hopefully should help me save a hell of a lot of time. Command Palette & Hotkeys The command palette (Cmd/Ctrl + P) gives you a searchable list of every action in the app. It’s that VS Code energy developers are already wired to love. Almost everything in Obsidian has a bindable hotkey too, so once you get your muscle memory dialed in, you barely have to touch the mouse. Very common feature. Themes & Appearance Out of the box, Obsidian has a solid dark and light mode. But the community theme library goes deep — there are themes that make this thing look like a hacker terminal, a Notion clone, a notebook app, whatever vibe you’re going for. Fully customizable via CSS snippets, too, if you want to get nerdy with it. $FREE.99 And all of that? Zero dollars. No subscription tier required. That’s the baseline, and it already clears the bar for most of what I was using Notion for. Now
May 16

Gimme More Gemma 4

One Step Closer to Sustainable AI Today, we are going to give props where props are due, and that’s with Google’s new Gemma 4 models. The Gemma 4 models are open source with an Apache 2.0 license; Anyone can use this model commercially or personally with no restrictions, allowing folks to build and sell applications, embed the model in their product, or clone and build upon the core model code without needing permission from Google. That’s not the only cool thing about Gemma 4, the models are TINY and pretty damn powerful. As you can see in the image above, there are 4 models total: E2B, E4B, 26B, and 31B. E2B & E4B These models are best suited for mobile and IoT devices. There are no other models available that can run natively on the user’s device without an internet connection and using only the device’s processing power. The immediate benefit of this breakthrough is that populations with little to no internet access can begin using AI for their own learning. For personal use and coding, the intelligence of these 2 models is not strong enough to do any daunting task, coding, or deep thinking, but they are more than capable of providing quick 1-2 liner responses for learning and informational purposes (we’ll get into the exact details of the breakthrough later). This also benefits people who might get lost on long expeditions in unknown & uncivilized territories. For example, one might need to get foraging info to assist their survival tactics. Though they were engineered with a target audience in mind. These models were engineered from the ground up for maximum compute and memory efficiency, and in collaboration with Google Pixel, Qualcomm, and MediaTek, they can run completely offline with near-zero latency on phones, Raspberry Pi, and NVIDIA Jetson Orin Nano. 26B & 31B Here’s where things get pretty interesting. The 26B & 31B models are aimed at more coding assistants and agentic workflows, research, and enterprise production apps. The 26B works decently on a Mac Mini (M4), and you can easily downgrade to the smaller models for much greater performance, depending on the task and context provided. But for open source models that are so small with an open license, the 26B and 31B swing well above their weight class, outcompeting models with 20× more parameters. By total parameter count, Gemma 4 31B is 24× smaller than GLM-5 and 34× smaller than Kimi-K2.5-Thinking, delivering comparable performance at a fraction of the footprint. The more remarkable story is the 26B MoE: it achieves 97% of the 31B's quality at approximately 8× less compute per inference step, with the 26B MoE reaching 40+ tokens per second locally versus the 31B exceeding 10 tokens per second. The pure flexibility and power at these model sizes unlock so many possibilities in the open source space, especially for people looking for a free and frictionless way to get into using AI without needing massive compute power. Much better for the environment, too! Gemma The Winna Yes, I misspelled “winner” on purpose, relax. Gemma 4 is the clear winner when it comes to open models. At least in my. eyes. There are other open-source and free models, such as Kimi-K2.5 and Qwen 3.6, that beat Gemma 4 in almost every category, but the gap is not large, and with the other models, you pretty much will need a powerhouse of a home setup or the power of an actual enterprise server to run these on. So, for the everyday layman, Gemma 4 should be the model forced upon the people. Yup, I said it. If you aren’t using AI agents or AI in general for coding, deep thinking, research, or science, then you need to be forced to use a model that has very little impact on the environment and your wallet. There’s no reason for anyone to be paying $20/month for high-energy prompts about what to make for dinner or how to talk to women……there are people that do the latter and all I can do is pray for them lol. But below is a nice little graphic of the 4 different model variations, their use cases, and the min amount of compute needed to run (not necessarily smoothly). There are some cool facts to read into about the technical achievements of Gemma 4 and I urge anyone with a deeper interest in knowing the nitty-gritty to read into their official docs → https://ai.google.dev/gemma/docs/core/model_card_4 as well as peep the video below: Pairing With OpenCode and Ollama Now that we have a general sense of Gemma 4 and its capabilities, I wanted to test things out myself with a quick local project that uses the Gemma 4 Model to build out an API for an AI chat. I wanted to include a UI as well for screen recording purposes so I looked into ways to make this happen all without paying a single penny (locally of course). This led me to OpenCode and Ollama. OpenCode This is pretty much the open-source and free version of “Claude Code” with the added ability to use any free or paid model for your operations. To be more detailed, OpenCode is an AI-powered terminal coding agent that sits on top of whatever local model you point it at. It reads your codebase, writes and edits files, runs commands, and iterates — all from your terminal. Critically, it’s model-agnostic, so you can point it directly at your Ollama endpoint and it uses Gemma 4 as its brain instead of a paid cloud model. The local setup was very straightforward, and you can get it installed with curl, brew, or an installer: Curl curl -fsSL https://opencode.ai/install | bash Homebrew brew install anomalyco/tap/opencode Node (NPM, PNPM, BUN, YARN) # NPM npm install -g opencode-ai # PNPM pnpm install -g opencode-ai # Bun bun install -g opencode-ai # Yarn yarn global add opencode-ai Once installed, you are all set to start using OpenCode as your agent of choice, but you’ll still need a vehicle to download, manage, and serve open-weight models like Gemma 4 on your local machine. That's where Ollama comes in. Ollama Ollama is the runtime layer in which you can download and serve open-weight models and API dependent models on your local machine. In our case, it wraps the model in a local REST API (mimicking OpenAI's API format) so any app can talk to it at localhost:11434. With the command below: # The model i used is a custom 26b model. You can swap that out with gemma4:latest or any other model version ollama run gemma4:26b-32k you get your model running. Ollama handles quantization, GPU/CPU offloading, and model versioning under the hood. For our example project, it's the reason we have zero cloud dependency and zero API costs, which is DOPE AF!! Local AI API Example Now let’s dive into the example project itself. The goal was to build a Local AI assistant API using only OpenCode and Gemma 4 as the model. That turned into two rounds of attempting that, both having their fair share of difficulties when it came to some of the logic. Now this task was pretty hefty for Gemma 4 in my opinion but I wanted to see how well it would do with creating an AI itself. The Qwen flop This one kinda sucked because for the second run, I wanted to use Qwen since it’s supposed to be better when it comes to agentic coding. But long story short, as soon as I started using the qwen3.6:27b, which is supposed to be good on machines with at least 17 GB of RAM. I have 24GB, and while it’s running, my activity monitor says it’s using 22.5 GB of my 24 GB…. Not only is it using more GBs than the model apparently was supposed to use (or maybe it is, and there’s a lack of architectural knowledge on my end), but it just doesn’t work at all. No exaggeration when I say I let it run for about 1 hr on a simple question, and it never even got past the thinking step. Clearly, my Mac Mini was not up for the task with Qwen, but that’s exactly why Gemma 4 is so damn cool. There’s literally a model for every kind of device. API V1 & V2 So, as I mentioned earlier, I did two rounds of running this, and I will say there were successes and failures in both runs that made them about equal in output quality. V1 With V1, the UI is where we were lacking. Hell, the UI was the biggest struggle for both runs since it insisted it use Svelete and SvelteKit as the frontend framework 😂. I know most of these models have very little Svelte in their training data, so any chance I get, I make sure to use Svelte and help the AI build their Svelte chops lol. But considering this project is local, I’m not helping the big 3 (OpenAI, Anthropic, X) train their models this time around. The other issue with this build was that the scaffolding was a bit off, and that caused confusion when giving further directions. However, it seemed to pick up the pieces pretty quickly on the first round, and I got a working API + UI pushed up to GitHub. Below is a quick screenshot of a random question I asked the V1 chatbot: For additional context here, the first question was asked with nothing in the “System Prompt” section, but for the second question, I added “Make sure your responses are fantasy-themed”, and the response was adjusted accordingly. The System Prompt section itself is unique to this version’s build, as V2 did not feel the need to add that feature 😅. But it gives a bit more control of the output for the user by allowing them to add some constants into their chat flow. The UI uses Svelte and took a couple of prompts to fix the errors present. I was personally still impressed by how well it did with Svelte compared to other big-name models I’ve used in the past. V2 Now with V2, the first run through of the prompt gave it a good head start with the UI, since there were already structured files in the first version. There were only 2 main issues with the second run through: it initially built the frontend in a “UI” folder instead of a “Frontend” folder, and the button + POST request was broken. The UI design, though, was a bit more compact, and in my opinion, that was a better look. The System Prompt sec
May 6

Deepfake Disco

This Is Where We Are…… It pains me to see society slowly but predictably corrode. We’ve had scammers and thieves long before the age of technology, but there’s just something uniquely dark about Deepfake technology, where many people can’t decipher the difference between the deepfake and the real person. Visual deception was the limit with deepfakes for a while; it was possible to mimic a face with a near 1:1 accuracy but as soon as voice came into play, the jig was up. Now, with the vast improvement in AI models and audio generation, voice deception is more accurate than ever, and it’s extremely worrying. The Baby boomer generation was typically the demographic scammed by phone salesmen or offshore scammers via phone or email, but now, with deepfakes being able to mimic voices of your loved ones, that opens the door for many of the younger generations to be scammed as well. So I want to educate as many folks as I can to help them combat the rise of deepfake scams, as well as give a quick overview of what deepfakes are in general and the problems around the tech. What Are Deepfakes? Deepfakes are images, videos, or audio that have been edited or generated using artificial intelligence, AI-based tools, or audio-video editing software. They may depict real or fictional people and are considered a form of synthetic media, that is, media that is usually created by artificial intelligence systems by combining various media elements into a new media artifact. That’s a general definition and should give you an idea of where we are headed. The faster and more intelligent we make AI, the better these deepfakes and scams will get. For a bit of history, an early project called the "Video Rewrite" program was published in 1997. The program modified existing video footage of a person speaking to depict that person mouthing the words from a different audio track.[34] It was the first system to fully automate this kind of facial reanimation, and it did so using machine learning techniques to make connections between the sounds produced by a video's subject and the shape of the subject's face. Fast forward some years, the deepfakes we know today stem from generative adversarial networks (GANs). It was developed in 2014 and published in a research paper by researcher Ian Goodfellow and his colleagues. A GAN is a machine learning model designed to generate realistic data by learning patterns from existing training datasets. It operates within an unsupervised learning framework by using deep learning techniques, where two neural networks work in opposition—one generates data, while the other evaluates whether the data is real or generated. The “Generator” creates synthetic content, and the “Discriminator” evaluates whether the content is real. This back and forth eventually makes the fake content look as real as possible. Think of it like sharpening a sword against a steel block. Every time the sword (the Generator) is run against the steel block (the Discriminator), the sword gets sharper. Deepfakes entered the mainstream in 2018, with the release of accessible open-source deepfake tools like DeepFaceLab. In 2023, the deepfake tool market skyrocketed, with a 44% increase in the development of these tools. It’s Pretty Messed Up Tech Deepfakes are f’d up….. Not much more to elaborate on there lol. While deepfakes could be used to make appropriate parodies, 9 times out of 10 it’s used for things like creating synthetic media of world leaders or creating content combining copyrighted media, along with other nefarious purposes. There is a huge concern amongst academics around deepfakes promoting disinformation, violence, and hate speech. Unfortunately, the creation of non-consensual explicit content of women has served as a motivating factor for the rising popularization of deepfake tools. The problem is rampant, with Security Hero reporting that in 2023, approximately 98% of deepfake videos online are explicit in nature, and only 1% of targets in that category are male. Researchers have also shown that deepfakes are expanding into other domains such as medical imagery. In this work, it was shown how an attacker can automatically inject or remove lung cancer in a patient's 3D CT scan. The result was so convincing that it fooled three radiologists and a state-of-the-art lung cancer detection AI. To demonstrate the threat, the authors successfully performed the attack on a hospital in a white hat penetration test.[37] Another example is a sophisticated scam using AI-generated video and voice that nearly compromised a well-known crypto developer linked to the Cardano ecosystem. The attacker impersonated Pierre Kaklamanos, Head of Digital Assets Adoption at the Cardano Foundation, during what appeared to be a legitimate Microsoft Teams call. The target, developer Big Pey, said the scam almost succeeded after he followed instructions during the fake meeting. There are plenty of other domains that this can have a negative impact on, escpecially is we continue down the line of tokenization. We’d then be faced with deepfake contracts and deeds, getting people to sign away assets and funds to the attacker without even knowing it. How to Protect Yourself Protecting yourself is going to be the best way to avoid being scammed or caught up in a deepfake scheme. So it’s good to know a handful of the things to look out for and the best practices to follow to not be a victim of fraud. Verify Before You Trust If you receive an unexpected call, video, or message from someone claiming to be a family member, colleague, or authority figure — especially one asking for money or sensitive information — verify their identity through a separate channel. Call them back on a known number, or reach out via text/email independently. Example: If "your son" calls saying he's broke and needs some funds wired immediately, hang up and call his actual phone number before doing anything. Establish a Family Safe Word One of the most practical defenses against voice deepfakes is creating a secret code word with close family and friends. If someone calls claiming to be them in an emergency, ask for the safe word. No legitimate loved one will be offended. Example: A family could agree that the word "pineapple" must be said if any family member ever calls asking for urgent financial help. Slow Down — Urgency Is the Red Flag Deepfake scammers rely on panic and urgency to short-circuit your critical thinking. If any call, video, or message is pressuring you to act right now, that's your cue to pause. Scammers don't want you to have time to verify. Legitimate emergencies almost always have a moment to double-check. Use Multi-Factor Authentication (MFA) Everywhere Even if a scammer uses a deepfake to impersonate you to a bank or service provider, MFA adds a critical extra barrier. A voice or face alone shouldn't be enough to authorize anything sensitive — and if a service is relying solely on those for verification, that's a concern worth raising with them. Facing This Issue Head On Deepfakes are here and here to stay. The threat and its consequences are very real and present. Deepfakes today are at the point of undermining trust in the online identity verification process that many organizations, especially in the financial sector, have come to rely upon. With more people than ever authenticating themselves using biometrics across all their devices, the growth in the malicious use of deepfakes can lead to a dire need to rethink authentication security within the next five years, or sooner. As shown in the examples, the barrier to entry for creating realistic deepfakes has dramatically decreased. From cloned voices to full video impersonations, deepfakes empower scammers and fraudsters in ways that are harder to detect and defend against. Understanding the threat is the first step to defending against it. With more end-user security training and by leveraging emerging deepfake detection tools, organizations and individuals can begin to fight back against this new threat. We also have to help our elders and loved ones who aren’t tech-savvy get up to speed with this rising threat, so they too can be prepared to combat deepfakes. If you want to keep up with my work or want to connect as peers, check out my social links below and give me a follow! * 🦋 Bluesky * 📸 Instagram * ▶️ Youtube * 💻 Github * 👾 Discord Get full access to Digital Dopamine at digitaldopaminellc.substack.com/subscribe
Apr 21

Hack w/ Me Episode 3: Linux Basics + VMs

Intro Sup folks, my last completed module was about the basics of Linux, so that’s what this episode will be about. However, I’ll be covering a bit more than we covered in the module. Within TryHackMe, we covered basic commands, working with the filesystem, shell operations, flags & switches, and automation, to name a few. But what I also want to cover is how to get a Virtual Machine booted up using “Kali Linux”, which is a Linux distribution designed for digital forensics and penetration testing. That will involve us getting familiar with UTM, a full-featured system emulator and virtual machine host for iOS and macOS (sorry, Windows users, it might not be a 1:1 comparison on how to start the VM locally). We’ll also go through the entire process of installation of the distribution and even try our hand at a fun lil script 😏. Before we get into the hands-on sections of this episode, let’s go over some Linux basics. Lil Linux Lore The name "Linux" is actually an umbrella term for multiple OS's that are based on UNIX (another operating system). Thanks to Linux being open-source, variants of Linux come in all shapes and sizes - suited best for what the system is being used for. For example, Ubuntu & Debian are some of the more commonplace distributions of Linux because they are so extensible. For example, you can run Ubuntu as a server (such as websites & web applications) or as a fully-fledged desktop. While the TryHackMe module uses Ubuntu, when we are setting up our own VM, we will be using Debian, since that’s what Kali Linux is based on. When it comes to the commands and execution, though, both distributions should function very similarly. "Linux" is often used to refer to the entire operating system, but in reality, Linux is the operating system kernel, which is started by the boot loader, which is itself started by the “Basic Input/Output System”/”Unified Extensible Firmware Interface” (BIOS/UEFI). The kernel assumes a role similar to that of a conductor in an orchestra—it ensures coordination between hardware and software. And no, I did not come up with that analogy myself lol. But enough of the technical jargon, let's get our VMS set up, then dive into some basics. Setting Up Your VM There are a handful of steps to properly set up both of the VMs we will be using in this episode, so I created a quick video to help walk through each step of the process while notating some of the known bugs when it comes to getting the “Kali Linux” VM set up. Quick Setup Summary UTM * We will need UTM as our emulator. Download and install it → https://mac.getutm.app Kali * Download the ISO image from their site → https://www.kali.org/get-kali/#kali-installer-images. Make sure you are downloading the right image for the architecture you’re on. * Follow their official guide once you have it downloaded → https://www.kali.org/docs/virtualization/install-utm-guest-vm/ ParrotOS * Download the pre-configured UTM from ParrotOS - Home → https://www.parrotsec.org/download/ * Follow their official installation guide → https://www.parrotsec.org/docs/virtualization/utm-configuration Once you have both of these VMs installed, we are ready for action! The Basics Commands Now, when it comes to commands, if you are familiar with macOS terminal commands, you should feel at home with Linux. Apple’s macOS is based on UNIX as well, so a lot of the commands for filesystem management, shell commands, and shortcuts should be identical. Let’s start with the very basic command echo. echo will output any text that we provide. Check out this screenshot below. You can see the simple command input and the output. There are various use cases for using echo from debugging to getting environment variables in a safe manner, and we will for sure be using some of them throughout this series. Another basic command is whoami to find out what user we're currently logged in as. That’s the only function there, lol, a one-and-done command. You can see in the screenshot above that I’m logged in as “mortaniel”. Next we have pwd which stands for “print working directory”, and this just prints out where you are currently in your filesystem within your terminal. The ls command prints out the contents of the directory you’re in. The last command I wanna mention here is cd, which lets you navigate into certain directories. Below you can see me navigate into “Desktop” and I used ls to list out the contents on my VM’s desktop, which is just a custom folder named “Best Folder”. There are TONS of other commands available to use, and we’d be here all day if I tried listing them out with their use cases. For instance, the find command is very useful and worth covering; I just have to spare the time to fit everything under an hour. It would be extremely boring too 😂, and besides, there are going to be commands I use later in this episode and will provide a quick definition of what it is when I reference/use them. So let’s move on to the next section, where I want to discuss SSH. Secure Shell: Operations & Deploy Secure Shell (SSH) is the common means of connecting to and interacting with the command line of a remote Linux machine. In TryHackMe, we deploy two machines: * The Linux machine * The TryHackMe AttackBox Where we go over common shell operations as well as how to use it to remotely log into the Linux Machine. So, what we’ll do is create 2 VMs locally to emulate what we’re doing in TryHackMe (or at least as close as possible). The main VM we will make should be a standard setup in terms of memory and storage allocation. This will be our “Attack Box”, and it will be using Kali Linux. The second VM we create will be our “Target Linux Machine”, and I’ll use ParrotOS for this one, to change it up a bit. You are more than welcome to use 2 Kali Linux VMs. Ubuntu, being the 2nd VM, is ideal if you can get it set up, but I’m having compatibility issues that I don’t feel like resolving, so I'm stuck with 2 different Debian-based distros. I’ll walk through how to set both up a bit later on, but our Attack Box will be where we get to test out some of these shell commands. I’m also going to walk through a cool and simple exploit to give an idea of what’s capable in the world of pen testing. A few of those operations are as follows: * &: This operator allows you to run commands in the background of your terminal. * &&: This operator allows you to combine multiple commands together in one line of your terminal. * >: This operator is a redirector - meaning that we can take the output from a command (such as using cat to output a file) and direct it elsewhere. * >>: This operator does the same function of the > operator but appends the output rather than replacing (meaning nothing is overwritten). If we want to get into more details on each operator: Operator “&” This operator allows us to execute commands in the background. For example, let’s say we want to copy a large file. This will obviously take quite a long time and will leave us unable to do anything else until the file is successfully copied. The “&” shell operator allows us to execute a command and have it run in the background (such as this file copy), allowing us to do other things! Operator “&&” This shell operator is a bit misleading in the sense of how familiar is to its partner “&”. Unlike the “&” operator, we can use “&&” to make a list of commands to run for example command1 && command2. However, it’s worth noting that command2 will only run if command1 was successful. Operator “>” This operator is what’s known as an output redirector. What this essentially means is that we take the output from a command we run and send that output to somewhere else. A great example of this is redirecting the output of the echo command that we learned in Task 4. Of course, running something such as echo wordsILoveToSay will return “wordsILoveToSay” back to our terminal — that isn’t super useful. What we can do instead is redirect “wordsILoveToSay” to something such as a new file! Let’s say we wanted to create a file, and I’ll name it newFile with the message “sup”. We can run echo sup > newFile where we want the file created with the contents “sup” like so: Now I personally don’t see much use of this other than allowing users to save command results directly to a file for later but I feel that’s something you’d just work on in whatever file you are writing code in. I could be missing the nice use case and maybe it’ll come to me in practice. That leads us to the “>>” operator. Operator “>>” This is pretty much an extension of the previous command. But instead of creating or replacing an entire file with the new command, this operator appends the output. These are just very basic Linux commands, and I’m sure I’ll find more use for them the more I dabble. There is a lot more basic knowledge that I feel is useful but to go into detail on each one would make this a very long episode. So in the next section, im just going to list the command, give a quick definition, and show an example screenshot if applicable. The File System Navigating the file system is extremely important, but can be summed up clearly, in which your file system is like your playing field in an RPG, table top, or digital. In that RPG’s playing field/world (the filesystem), there are many paths that you can take once you leave home (different paths like /Home/Desktop/Pictures & /Home/Downloads/cool_img.jpg). But you can always come back home and even further, go back to your root(s)…aka /~. I haven’t seen anyone make a similar analogy, so I’m gonna call dibs on that until further notice 😏. But you want to be able to navigate your file system(s) effortlessly, because sometimes time is not on your side when trying to execute an exploit or defend yourself from one. Termin
Mar 23

Tokenizing Private Credit, Real Estate, National Debt, and Everything Else

Blockchain and Tokenization Popularity Boom Tokenization has become one of the new buzzwords in the world of finance. Your 401(k) may already be invested in assets you've never heard of, through technology you don't understand — and the people making those decisions didn't ask your permission.There are a lot of people who barely know much about blockchain, and it’s been around even before cryptocurrency was a thing, and tokenization is just an extension of digital assets. So I feel like I should go over both just a bit. Blockchain A blockchain is a digital ledger, which is a record-keeping system, that stores information across a network of computers rather than in a single centralized location. Think of it as a shared spreadsheet that thousands of computers maintain simultaneously, where every entry is permanent, time-stamped, and visible to participants on the network. Some key features and benefits are: * Decentralization: No single institution (no bank, no government, no company) controls the ledger. It’s maintained by a distributed network of computers (called “nodes”). * Immutability: Once a transaction is recorded, it cannot be altered or deleted. Every entry is cryptographically linked to the one before it, forming a “chain” of “blocks” — hence the name. * Transparency: On public blockchains, every transaction is visible to anyone who looks. This is a feature for accountability, but also a concern for privacy, since financial activity becomes traceable. * Smart contracts: Blockchains can execute automated agreements — code that says “if X happens, then do Y.” For example, a smart contract could automatically distribute rental income to token holders every month without a human intermediary. Tokenization Tokenization is the process of creating a digital token on a blockchain that represents ownership of real-world assets like real estate, art, or stocks, as well as intangible assets such as intellectual property or voting rights. Tokenization enables easier asset transfers, ownership verification, and fractional ownership. Imagine a commercial building worth $10 million. Traditionally, you'd need millions of dollars and a team of lawyers to buy it. With tokenization, the building's ownership can be divided into 10 million tokens worth $1 each. An investor could buy 100 tokens for $100 and own a tiny fraction of that building, receiving proportional rental income and being able to sell those tokens on a digital marketplace. Now this may sound like a scenario where the average Jamal has a seat at the big dawg table, but that fractional piece of ownership will never outweigh the millions or billions of dollars the elite will put in all at once, maybe even taking ownership of all available tokens for that digital asset themselves. How Tokenization Connects Real-World Assets to the Blockchain Let’s cover a bit on exactly how Tokenization Connects Real-World Assets to the Blockchain: * The asset exists in the real world — a piece of real estate, a corporate loan, a Treasury bond, shares in an ETF, etc. * A legal structure is created that ties the digital token to legal ownership rights over that asset. This is the critical (and often fragile) link, because the token is only as good as the legal framework backing it. * Tokens are then issued on a blockchain, where they can be bought, sold, held, or used as collateral — 24 hours a day, across borders, without traditional intermediaries like brokers or clearinghouses. But this presents its own issues with cyber criminals and hacking. * Last but not least, Smart contracts automate functions like interest payments, dividend distributions, compliance checks, and transfer restrictions. But what is being tokenized right now, you ask? * U.S. Treasuries and money market funds (~$12 billion tokenized) — led by BlackRock’s BUIDL fund and J.P. Morgan’s MONY fund * Private credit (~$9 billion tokenized) — corporate loans that were traditionally opaque and illiquid * Real estate — commercial and residential properties fractionalized for smaller investors * Commodities — tokenized gold and trade receivables * ETFs and fund shares — increasingly being explored for on-chain issuance Who Is Leading The Financial Tokenization Charge Like I just mentioned, tokenization converts ownership of physical or financial assets into digital tokens on a blockchain (real estate, loans, Treasury bonds, ETFs), and we have big players like BlackRock, J.P. Morgan, Goldman Sachs, Franklin Templeton, and Apollo Global Management, leading the charge. The tokenized real-world asset market surpassed a staggering $26 billion by early 2026, which is a fourfold increase from early 2025, with private credit and U.S. Treasuries dominating. The Private Credit Problem Large lenders, including BlackRock, Blue Owl, and J.P. Morgan, have restricted investor withdrawals from private credit funds amid rising interest rates and borrower distress. Businesses that took on these loans are struggling to service their debt, and what most people don’t know is that tokenization is being positioned as a solution. The plan is to create secondary markets for these illiquid loans, fractionalize them, and potentially offload the risk to a broader pool of investors. Some might call this a genuine innovation in the digital landscape of finance, but I call it transferring risk from institutional balance sheets to less sophisticated investors. The private credit market is sitting at a staggering $3 trillion, and investors want out, which could cause major ripple effects in the broader markets. Now I can go deep into a rabbit hole on the issues in the private credit market alone, but let’s stick to our focus on the tokenization of all these connected issues. The 401(k) Pipeline Back in August 2025, President Trump signed an executive order directing the Department of Labor to open 401(k) plans to alternative investments, including private equity, private credit, and digital assets. Essentially, just giving America’s $13.9 trillion in defined-contribution retirement plans to private-asset giants like Apollo, Blackstone, and BlackRock. Target Date Funds — where most workers’ 401(k) money sits by default — were designed for daily liquidity, transparency, and public markets. They were never built to house illiquid private equity or gated real estate. As one longtime fiduciary advisor put it: “This quiet push isn’t democratization. It’s risk transfer.” And a lot of workers typically have little or no say in how their plan providers allocate funds. The risks are substantial on multiple fronts: * You have higher fees than traditional index funds. * Your money is locked up for years. * It’s less transparent in terms of knowing which companies took out these risky loans. * And there’s no guarantee of performance. Legal experts warn that many plan committees lack the expertise to evaluate private market investments, and attorneys are already watching for ERISA fiduciary violations — ERISA being the 1974 federal law specifically designed to protect workers in retirement plans like these. The Security Problem No One Wants to Talk About Tokenization’s promise depends entirely on the security of the systems holding these assets. And right now, those systems are under siege from every direction — nation-states, organized crime, AI-powered scam operations, and even physical violence. State-Sponsored Theft: The North Korea Problem North Korean hackers, operating under the umbrella group known as Lazarus Group, stole $2.02 billion in cryptocurrency in 2025 alone — a 51% increase from the prior year and their all-time record. Their cumulative total now exceeds $6.75 billion. The single largest heist was the February 2025 Bybit hack: $1.5 billion in Ethereum stolen by compromising a third-party wallet provider’s developer through social engineering. The FBI formally attributed the attack to North Korea’s TraderTraitor unit. What makes this especially relevant to tokenization is that these hackers aren’t breaking the blockchain itself. They’re targeting the human and operational layers around it — the developers, the wallet software, and the interfaces people actually use. When real-world assets like Treasury bonds, real estate, and private credit are tokenized and held in similar infrastructure, the attack surface for nation-state hackers grows enormously. We’re no longer talking about stolen cryptocurrency; we’re talking about the potential theft or manipulation of tokenized deeds, loan obligations, and retirement fund shares. North Korea is the only country in the world known to use state-sponsored hacking primarily for financial gain, and the proceeds fund its nuclear and missile programs. A senior Biden administration official estimated that roughly 50% of North Korea’s foreign-currency earnings come from cybercrime. Now, just think about your 401(k) assets becoming tokenized on the same kind of infrastructure these hackers are already systematically exploiting; the risk is no longer theoretical. The AI-Powered Fraud Explosion AI is becoming embedded in everything and is becoming more of a security risk for our everyday lives today, without even considering tokenization. Crypto scam losses hit an estimated $17 billion in 2025, according to Chainalysis. These AI-powered scams — using deepfake video calls, cloned executive voices, and automated social engineering — extracted 4.5 times more money than traditional scams. Impersonation scams surged 1,400% year over year. One investor lost $284 million in a single phishing attack after scammers impersonated hardware wallet support staff. As tokenized assets become more mainstream, these same techniques will be weaponized against retail investors, plan administrators, and the platforms managing tokenized 401(k) assets. Imagine AI-generated deepfakes impersonating your retirement plan administrato

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Creator

Digital Dopamine
Years Active

2026
Episodes

13
Rating

Explicit
Copyright

© Kyle Leonard
Show Website

Digital Dopamine