Tiph

Tiph is now incorporated in the UK. Expanding our reach across Europe, with the infrastructure to match. Same work. Wider reach.

@MrBeast Hey @WhatsApp, add polls to WhatsApp status

Everyone on earth takes a private vote by pressing a red or blue button. If more than 50% of people press the blue button, everyone survives. If less than 50% of people press the blue button, only people who pressed the red button survive. Which button would you press? BE HONEST.

@claudeai You kidding me🤦‍♂️

With the Autodesk Fusion connector, designers and engineers can create and modify 3D models through conversation.

Claude now connects to the tools creative professionals already use. With the new Blender connector, you can debug a scene, build new tools, or batch-apply changes across every object, directly from Claude.

@depression2019 This isn’t about survival lad. That’s how you died on the door plank on titanic

Thought we would let you know.

LLM Knowledge Bases Something I'm finding very useful recently: using LLMs to build personal knowledge bases for various topics of research interest. In this way, a large fraction of my recent token throughput is going less into manipulating code, and more into manipulating knowledge (stored as markdown and images). The latest LLMs are quite good at it. So: Data ingest: I index source documents (articles, papers, repos, datasets, images, etc.) into a raw/ directory, then I use an LLM to incrementally "compile" a wiki, which is just a collection of .md files in a directory structure. The wiki includes summaries of all the data in raw/, backlinks, and then it categorizes data into concepts, writes articles for them, and links them all. To convert web articles into .md files I like to use the Obsidian Web Clipper extension, and then I also use a hotkey to download all the related images to local so that my LLM can easily reference them. IDE: I use Obsidian as the IDE "frontend" where I can view the raw data, the the compiled wiki, and the derived visualizations. Important to note that the LLM writes and maintains all of the data of the wiki, I rarely touch it directly. I've played with a few Obsidian plugins to render and view data in other ways (e.g. Marp for slides). Q&A: Where things get interesting is that once your wiki is big enough (e.g. mine on some recent research is ~100 articles and ~400K words), you can ask your LLM agent all kinds of complex questions against the wiki, and it will go off, research the answers, etc. I thought I had to reach for fancy RAG, but the LLM has been pretty good about auto-maintaining index files and brief summaries of all the documents and it reads all the important related data fairly easily at this ~small scale. Output: Instead of getting answers in text/terminal, I like to have it render markdown files for me, or slide shows (Marp format), or matplotlib images, all of which I then view again in Obsidian. You can imagine many other visual output formats depending on the query. Often, I end up "filing" the outputs back into the wiki to enhance it for further queries. So my own explorations and queries always "add up" in the knowledge base. Linting: I've run some LLM "health checks" over the wiki to e.g. find inconsistent data, impute missing data (with web searchers), find interesting connections for new article candidates, etc., to incrementally clean up the wiki and enhance its overall data integrity. The LLMs are quite good at suggesting further questions to ask and look into. Extra tools: I find myself developing additional tools to process the data, e.g. I vibe coded a small and naive search engine over the wiki, which I both use directly (in a web ui), but more often I want to hand it off to an LLM via CLI as a tool for larger queries. Further explorations: As the repo grows, the natural desire is to also think about synthetic data generation + finetuning to have your LLM "know" the data in its weights instead of just context windows. TLDR: raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the domain of the LLM. I think there is room here for an incredible new product instead of a hacky collection of scripts.

4 years. 3 focus areas. One consistent approach. Strategy. Design. Systems.

New project live. Built a website and operations management system for Twehues Umzüge, a moving company based in Ingolstadt, Bavaria. Service pages, pricing, quote requests, and a back-end system to manage inquiries and shipment tracking. Live at twehuesumzug.net

5/For businesses integrating AI right now: this is a reminder that the tools you choose carry reputational weight. Infrastructure decisions are no longer just technical. They're becoming policy decisions.

4/Whether you agree with either side or not, something shifted this week. AI companies are no longer just product companies. They're being asked to take positions on war, surveillance, and governance. That's a different game entirely.

1/OpenAI signed a Pentagon deal hours after Anthropic got blacklisted for refusing one. Altman then admitted it was "rushed." This week in AI governance is a lot.

Tiph has partnered with KauntaBook, Uganda’s top accounting software that helps businesses move from handwritten books to digital bookkeeping. We’ll handle their marketing, communications, and sales, helping more entrepreneurs track every shilling and manage stock efficiently.

From a one-person agency at @Makerere to a team building brands with design, strategy, and storytelling. 3 years in, and we’re just getting started. 🚀

Most teams think a CRM is a database. They’re right. But they’re also missing the point: it’s a machine for predictable follow-up, and that is how revenue happens.

#MarketingVsTech Marketing Team: Can we add a chatbot? Tech Team: Sure, what should it say? Marketing Team: We’ll decide after it’s live. Tech Team: So… it just stares at people? Marketing Team: Yeah, but friendly.

AI personalization is reshaping ROI. Scale smarter, convert better, and cut costs. Question: Which metrics are you automating with AI today?

AI reduces cognitive overload: buyers see only relevant content, decisions get easier, and trust in your marketing grows.

AI-Powered Personalization is transforming B2B SaaS marketing in 2025. Here’s how smart marketers are boosting conversions and cutting costs. 👇