Sr. Software Engineer | Drinks Coffee. Experiments: nullhaven.studio/

Remote
While managing my home network, I realized how many people are "hands-off" with their own security. Why should it be a tech-only task? 🤔 I don’t want my family needing me every time they manage network access. Imagine a network that's actually fun to manage—gamified guest access, learn-to-earn, TTL and lease control, IP masking, and secure DNS blocking! 🎮 With Project Sentinel, built on Elixir and a Raspberry Pi, you can control device access without sharing personal network details. Admin control is seamless, secure, and bloat-free. I’ll be tweeting about each feature and the problems I'm solving—working toward an open-source project anyone can use! 💡 Think less “DNS sinkhole” and ad-blocking, and more seamless management with a fun, user-friendly UI—all from a pocket-sized SBC plugged into your main router to manage your home network. #ProjectSentinel #Elixir #DNS #Networking #BuildingInPublic
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Do people not care about the why anymore? Why a decision was made. Why something exists in a build. When you’re hands-on, you can’t avoid those questions. But a lot of people just generate, let the visual output speak for itself, and never ask why. The irony: we’re social creatures. We use social skills to prompt, yet skip the conversation about why in the moments that are supposed to be collaborative.
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Torean “_TrojanMorse” Joel retweeted
If you’re building a agent Always go the harness route first. Do not build a complicated 20 step workflow using a bunch of if else statements or langgraph Use a harness as your agent eg. Claude code, codex, deep agents, glean etc You’re just shooting urself in the foot off the bat Trust me, prompting works surprisingly well
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Torean “_TrojanMorse” Joel retweeted
🚀 Introducing DeepInfer, our in-house inference framework, now fully open source! Powering DeepSeek-V4.1-Flash at scale with high throughput and low latency. We’re sharing our production stack to help the community lower compute costs, reduce energy use, and make AI more affordable. 🔗 github.com/deepseek-ai/DeepI… 1/4
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The dust build on my Mini network setup is a obvious indicator of how much AI has taken over my day to day. Time for a clean.
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A week ago my AI coding cost ~$57 a day. The last two days: ~$11. Early numbers, but the pattern's clear. TLDR: Opus plans with me (optional review), then one DeepSeek Flash executor runs the spec 🧵 🧵
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You choose how much checking each run gets. Light runs just go. Normal runs get the plan looked over, and a stronger model steps in if a step gets stuck. Careful runs have every change reviewed. More checking costs more, so you only pay for it when the work needs it.
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Next up: a look at my monthly usage, then a closer look at the spec and the models I use. The goal is to get as close as I can to using straight Opus, for a lot less. I'll show it in action with some visual experiments along the way. Follow along.
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Nothing beats a terse response from your agent.
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I’ve gotten so much value out of even just the $20 @ollama plan. Being able to experiment with the latest open-weight models without ever worrying about uptime is amazing. Plus my room no longer doubles as a sauna every time I want to try a new model.
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Finally ready to rework a old game idea I had in a repo for over 9 years. With the tooling now along with some of the research, this is a good time to experiment and see the cost of making something a little more "serious" while learning and tuning my workflow.
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Genuine question: how do people post so much on here? I know some of it is automation and bots. I’d just like to share interesting things and have real conversations that don’t end in a call-to-action or a sales pitch. How do you find the good conversations?
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A read through im looking forward to checking out from Google. github.com/google-research/h… I'm sure some valuable insights, to help model a better workflow with models.
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Price per token is the easy number. What matters to me is cost per working result. Retries, time until it actually runs, whether it holds up the next day. A cheap model that needs three tries isn't cheap. *DeepSeek walks into the room
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Although it might not be the main model I plan and spec with. On smaller or manual steps involving me chatting or a general workhorse, likely what I'll be experimenting more with.
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97% of my agent's input comes from cache. That turns about $8.1k of tokens into about $420. So why is my cost per turn still rising? 30 days of data, and what I'm changing before I touch models:
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So next: phases between goals with the spec as the handoff, small edits instead of full writes, and DeepSeek workers doing reads and edits in their own context. The handoff becomes a measured step: tokens in, tokens back, cache writes.
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Models come last. Opus is 90% of spend because I pinned it for one feature. Haiku and the open-weight models go; DeepSeek V4.1 Flash stays for now.
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