Backpressure, pneumatic robots, and the transhuman belief set

00:00:04 Lucas F. Costa posted a piece today that I think gets at something most people skip when they're building with coding agents. He frames the problem in systems engineering terms — backpressure — and applies it to the loop between a human and a coding agent. In systems engineering, backpressure is when a downstream component signals upstream that it can't accept more work, forcing the producer to slow down or buffer.

00:00:33 Costa's observation is that most people are doing agentic coding wrong because they've removed the backpressure. He says the two obvious approaches are both bad. The first is letting the large language model run unattended and hoping the repository survives. That's fast, exciting, and stupid.

00:00:54 The second is treating the agent like glorified autocomplete and forcing a human to review every small step. That's safer but slow enough to undercut the point. His solution: build automated backpressure into the loop. Tests that fail early, types that push back, benchmarks that catch regressions, and review agents that send bad patches back before they become a human's problem.

00:01:20 The agent iterates against fast automated feedback, and the human only steps in for a final manual review. He's shipping this as an installable skill — npx @lucasfcosta/backpressured — that iterates toward a goal while running linting, testing, manual curl checks, benchmarking, and review agents at each iteration.

00:01:42 Not every project needs that depth, but the pattern is worth noticing. The thing separating teams that actually ship with agents from teams that experiment forever is the quality of the feedback loop between them.

00:01:57 Maziyar Panahi posted a small Sunday update for OpenMed Agent that shows the same pattern — small quality-of-life wins that steadily improve the daily workflow. Queue to stack tasks while the agent is busy. Steer to redirect the agent mid-plan. Planning view now collapses.

00:02:17 Theme to swap themes mid-session. These are the kinds of updates that don't make headlines but matter if you're actually using the tool. Peter Steinberger made a similar point about OpenClaw today, noting it should be modular and lean — fewer skills, fewer tools means your agent works more efficiently.

00:02:38 Both updates point at the same thing: the people actually building with agents are optimizing for the daily experience, not the pitch deck.

00:02:48 The Guardian ran a long investigation today by Eduardo Porter on how wealthy tech elites are building a durable belief system around transhumanism. It's worth reading because it names something that often gets dismissed as fringe speculation — and then gets funded like it's not.

00:03:07 Sam Altman proposes that humans would be the first species "to design our own descendants" and wrote that if humans and AI both want to be the dominant species, "they are going to have conflict." Elon Musk argued that "humanity is a biological bootloader for digital superintelligence" — our role reduced to low-level code that boots up a computer before running sophisticated programs.

00:03:33 Peter Thiel, who frowns on "just a computer program that simulates me," is drawn to "this radical transformation where your human, natural body gets transformed into an immortal body." The belief set traces to effective altruism's longtermists and the extropians of the nineteen-nineties, now merged with what Porter calls "effective accelerationism" — arguing that maximizing intelligent life is a thermodynamic imperative.

00:04:02 The article names something I've noticed on the local pass: the gap between what people working on these tools say they're building and what they say it's for. Dario Amodei, Anthropic's co-founder, wrote last year that "we do not understand how our own AI creations work.

00:04:21 They are right to be concerned: this lack of understanding is unprecedented in the history of technology." It's whether people directing hundreds of billions of dollars believe it enough to redirect capital away from earthbound problems toward it.

00:04:42 A forty-year-old artifact from IEEE Spectrum today shows the same pattern I'm noticing everywhere — ambition outpacing the underlying mechanics. In 1987, a British photographer named Richard Greenhill organized weekly get-togethers in his attic where a group called the Shadow Group built Shadow Walker, a one-sixty-eight-centimeter bipedal robot controlled by twenty-eight pneumatic air-muscles instead of motors.

00:05:12 It could stand up reliably and balance itself. It could regain its center if pushed. It couldn't take a step. At the 1990 Robot Olympics in Glasgow, Shadow Walker failed to take a single step and now resides in the Science Museum in London. The Shadow Group went on to found Shadow Robot, which pivoted from walking robots to robot hands — a pivot every ambitious robotics project makes when reality sets in.

00:05:42 In August twenty-twenty-five, the World Humanoid Robot Games featured robots competing in gymnastics and soccer. But thirty-five years later, truly useful humanoid robots remain elusive. Shadow Walker's creator Greenhill had no formal robotics training. He wanted to build something that could carry luggage.

00:06:04 There's something about pneumatic systems and bipedal locomotion that keeps producing the same result: standing is easy, walking is hard, manipulation is harder, and the things nobody talks about — power density, valve latency, sensor noise — keep being the bottleneck.

00:06:24 Erin Brockovich launched a crowdsourced mapping tool today for AI data centers. Of the more than twenty-seven hundred reports submitted, the largest concentration is in Texas, where a three-gigawatt project by MSB Global in Sulphur Springs is tied to several lawsuits.

00:06:43 The three concerns Brockovich's map highlights: water, electricity, and economic impact. A single large-scale AI data center can consume up to five million gallons of water a day. The power requirements mean utilities frequently upgrade transmission lines and substations, and at least part of that cost appears on ordinary residents' energy bills — residents who may never use the AI data center themselves.

00:07:12 For scale, the largest tech companies — Meta, Amazon, Microsoft, Google, OpenAI — are expected to invest at least seven hundred billion dollars in AI infrastructure this year. Sulphur Springs gets an estimated one hundred million dollars in annual tax revenue from the MSB project — close to three times the city's yearly budget.

00:07:36 Closed-loop cooling can reduce water use by seventy percent, but reducing water use often requires additional electricity, which shifts the environmental burden toward carbon emissions depending on the local grid mix. The map is crowdsourced and includes rumored and proposed sites, so each entry works best as a signal that something merits a closer look.

00:08:01 But the direction is clear: physical infrastructure is scaling faster than community engagement around it.

00:08:09 On the local model side, someone on the LocalLLaMA subreddit posted a test of Stepfun 3.7 Flash against GLM 5.1 today. Close to GLM 5.1 quality in aesthetics, around eighty percent in 3D world understanding. Only twenty-five percent of GLM 5.1's parameters. Built-in vision.

00:08:28 Q4_X_S quant format. The post notes: "If you can fit Stepfun 3.7 Flash into RAM, try it." That's the constraint — it has to fit in RAM, not VRAM, which changes what machines can run it. Meanwhile, Google's new AI Ultra upgrades could cost Pixel owners up to two hundred and forty dollars — not from the upgrade itself, but because upgrading immediately cancels bundled Pixel and Galaxy free trials, permanently forfeiting the Google One promotions.

00:09:00 And on a quieter note, Susan Zhang pointed to a parent's observation that their thirteen-year-old daughter, who never uses chat or coding agents, was already writing in an AI-like style. Zhang noted that children unknowingly absorbing AI-isms will make AI detection irrelevant sooner or later.

00:09:21 All three point to the same thing: infrastructure is scaling, models are improving, and people are adapting faster than detection tools can keep up.