I predict that in 5-10 years every household will have a local AI server that serves all the AI workload of the house, digital or robotics. It’ll be as common as a WiFi router or a fridge it creates a private mesh network to link all the devices in the house and does inference completely locally, with built in data vault resistant to remote data hacking or physical tampering. That way you can have every single piece of information about you and your family stored in there and never leaves it, and AI can use them to provide most accurate and customized services

I've been de-Googling and transitioning to encrypted, self-hosted, or privacy-first everything over the last ~2 years. What I changed and notes: 1) Email: Gmail → @ProtonMail For personal, this was easy. Everything from my old gmail is autoforwarded to a domain-based email address I own hosted via Proton. If Proton goes away tomorrow, I still own the email and can point it to another, hopefully encrypted, email provider. For business, this is not yet a viable option mainly due to email deliverability and share docs are not quite as good with Proton yet as Google Docs/ Sheets, etc. Will continue to reevaluate over time. 2) Photos: Google Photos/ iPhotos → @enteio Tedious but surprisingly easy to do. Always have a local backup of anything you really care about, obviously. 3) Calendar → Proton I sync my gmail based work calendars into this and personal events are only detailed here. Proton is all I look at. 4) Chrome → @brave Chromium w/o Google, basically. Seamless transition. 5) Passwords & 2fa → ProtonPass, Yubikey Stop using your browser 6) Direct Browsing → Firewall + Persistent Rotating VPN Probably the most technical thing on this list. 7) Network Router / Modem → Owned Router/ Modem (Bonus: everything got way faster.) 8) Keep Notes & DropBox → @StandardNotes End-to-end encrypted. Make sure you keep your keys, lock yourself out even they can't help you 😅 9) Podcasts: Spotify → @antennapod If you're primarily a podcast listener there's no reason to use Apple or Spotify for this - Antenna Pod is seamless, free, no ads. (h/t @WatchmanPrivacy for the rec) 10) YouTube → NewPipe Android. No ads, faster, and you can download YouTube videos for free to watch offline. (Downside - not sure if this will continue to work when Android gets locked down.) 11) Giving out my email address → Aliases for everything (Proton, @keepitcloaked make this simple) One-touch "Disable email address" to kill SPAM is nice. It all flows into your inbox so there's no change for you, just puts you back in control. 12) Giving out my phone # → Fake # per service (Cloaked, Voip, etc) Also a fun way to figure out who resells your # (looking at you, electric company). I've killed my old # and have a new one I don't give out to anyone I would not call in the event of an emergency, but I probably take things to an extreme most people do not. The biggest privacy "levers" are the simplest and don't require an app transition at all - stop sharing your PII - esp your address, personal email, mobile #. Don't centralize your entire life with 1 company (Google, Apple). -- This only scratches the surface, but sharing to illustrate that you can avoid giving a single corporation or two control over your digital life. As we move from a world of creepy-but-mostly-benign-surveillance‑based business models to ubiquitous data sharing, warrantless surveillance and centralization - choosing free/open-source software and decentralizing the tech you use is a vote for personal autonomy. The tradeoff to convenience is that it almost always comes at the cost of freedom.

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Most chatbots look great in demos… then collapse the second real users show up. The difference isn’t a better prompt. It’s treating the bot like software: grounded GraphRAG retrieval, narrow memory, clear ontology, and real operational controls. falkordb.com/blog/how-to-bui…

From prompt → context → harness → loop → graph engineering: The list keeps growing, and every new term gets treated as a replacement for the last one. In reality, however, each layer wraps the one before it, and the cleanest way to tell them apart is to ask what a single unit of work looks like. > Prompt engineering is the message: The model remembers nothing before this call, so the prompt has to carry the full universe of what it needs. a role, the background, the instructions, a few examples, and a format. When the output falls short, the skill is working out which ingredient lets you down, not rewriting the instructions every time. The unit of work is one input. > Context engineering is the memory: Across many steps, the window is finite, and the available information is not, which forces a curation step. A curator keeps what matters, compresses what is useful but bulky, and drops the rest. Good curation is mostly about knowing what to throw away, not packing more in. The unit of work is what stays in the window. > Harness engineering is the machine: On its own, a model just generates text. The harness gathers what it needs, runs it, calls tools or sub-agents, and verifies the result with tests or a judge. That verify step is the entire difference between calling an api and running an agent. The unit of work is one pass through the machine. > Loop engineering is the run: One pass rarely finishes the job, so something has to decide whether to run the machine again. That decision needs a goal defined upfront, brakes like max iterations and budget caps, and a completion check that is automated rather than felt. An agent that stops asking for tools has ended its turn, which is not the same as finishing the task. The unit of work is the whole run. > Graph engineering is the coordination: Once several loops have to work together, you need to say what runs when, what runs in parallel, and who checks whom. Nodes do the work, edges decide what runs next, and shared state flows between them. A single loop is just a one-node graph with an edge pointing back at itself, which is why graphs govern loops instead of replacing them. The unit of work is the whole job. Here is the part that ties it together. Prompt and context both live inside the harness gather step. The harness is one pass, the loop decides whether to run that pass again, and the graph decides which loops run at all. Zoom out, and the unit of work gets bigger. Zoom in, and you are back at the prompt. That also tells you where to debug. Find the layer whose unit of work broke, then fix that layer. The prompt is the easiest layer to edit, which is why it keeps taking the blame for failures that live three layers up. My co-founder published a deep dive on graph engineering, covering the core idea, how to get started, shared state, routing you can trust, and when a graph is genuinely overkill. Read it below.

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Layers of Meaning: Context Graphs, Graph Memory, and Ontologies for AI What does it take to build an AI system that actually knows what it means? Not what it says. Not what it retrieves. What it means. Everyone is building context layers and ontologies for AI now. Context graphs are AI’s next trillion-dollar opportunity, we are told, and ontology is the secret sauce. The term shows up in product launches from Atlassian, AWS, Databricks, Google, Microsoft, Neo4j, and Snowflake. There’s just one problem: not everyone means the same thing when they talk about context layers or ontologies. Slightly ironic, considering context layers and ontologies are meant to address, well, meaning. What’s underneath this wave is something the graph and semantics community will recognize: a mass convergence on problems that have been worked on for decades, now arriving at scale, under new branding. Forrester traced the context graph lineage 40 years back to Enterprise Architecture. Deborah McGuinness wrote “Ontologies Come of Age” in 2003. The Semantic Web market reached $2.71 billion and is growing at 23% CAGR. This is not new territory. What is new is the scale, and the urgency. Scale changes the calculus. When every major data platform ships an “ontology” feature, the word migrates. When every startup promises a context layer, the concept stretches. When every agent framework claims graph memory, the question becomes: whose meaning survives the handoff between systems? Who owns the ontology? More fundamentally – what do we talk about, when we talk about ontology? In this issue of the Year of the Graph, we follow three converging threads: the architecture of the context layer, the mainstreaming and quiet dilution of ontology, and the emergence of graph memory as enterprise infrastructure. They converge on a single point: graph structure is necessary, but not sufficient. What matters is who gets to decide what the data means, and whether meaning can move along the data. 📋 Table of Contents The AI Context Layer: Three Types of Context, Three Different Problems Context, Graphs, and Semantic Layers Context Graphs are a Convergence, and Convergence Needs Architecture Ontologies for AI, by AI Everyone Has an Ontology Now Ontologies Come of Age (again) Ontology and Semantics Market, Education and Tools The LLM Wiki Pattern: From Personal Knowledge Graphs to Enterprise AI Knowledge Graphs, Memory and Context Graph Memory Structure Knowledge, Graphs, RAG, and Agentic AI Graph Databases Become Graph Engines Graph Data Is Table Stakes: Why Meaning Is the Real Differentiator Graph data is table stakes. What matters is how the meaning of the data can be captured, shared, and utilized. 👉yearofthegraph.xyz/newslette… #KnowledgeGraph #GraphMemory #Ontology #ContextGraphs #GraphAI #EnterpriseAI