β¨ Read this trending post from Hacker News π π **Category**: π‘ **What Youβll Learn**: Abstract Objective To assess the effects of didgeridoo playing on daytime sleepiness and other outcomes related to sleep by reducing collapsibility of the upper airways in patients with moderate obstructive sleep apnoea syndrome and snoring. Design Randomised controlled trial. Setting Private practice of a didgeridoo instructor and a single centre for sleep medicine. Participants 25 patients aged > 18 years with an apnoea-hypopnoea index between 15 and 30 and who complained about snoring. Interventions Didgeridoo lessons and daily practice at home with standardised instruments for…
π₯ Explore this must-read post from Hacker News π π **Category**: π **What Youβll Learn**: White Rabbit provides sub-nanosecond accuracy and picoseconds precision of synchronization for large distributed systems. It also allows for deterministic and reliable data delivery.White Rabbit allows you to precision time-tag measured data and lets you trigger data taking in large installations while at the same time using the same network to transmit data.sub-nanosecond synchronizationconnecting thousands of nodestypical distances of 10 km between nodesEthernet-based gigabit rate reliable data transferfully open hardware, firmware and softwaremulti-vendor commercially produced hardwareContactMaciej LipinskiTagsDocumentation White Rabbit π₯ **Whatβs your take?** Share your thoughts…
π₯ Discover this awesome post from Hacker News π π **Category**: π‘ **What Youβll Learn**: Jira IS Turing-Complete Nicolas Seriot Computation > Jira is Turing-Complete Building a Minsky Machine in Atlassian Automation22nd May 2026 Engineering folklore holds that Jira (Atlassian's project-tracking tool) is Turing-complete. Existing claims point vaguely at automation features without exhibiting a reduction. This article supplies a proof, with setup instructions and execution trace. Mapping the Computational Model A Minsky register machine needs only two unbounded counters and a finite set of labeled instructions: INC r; goto S DEC r; if r == 0 goto S else goto…
β¨ Explore this awesome post from Hacker News π π **Category**: π **What Youβll Learn**: I think the reason so many people don't understand how big AI is going to be is that they don't understand that everything is an algorithm.Specifically, they don't realize that companies are just a collection of algorithms.What do I mean by that?Well, let's say that your company takes pictures, cleans them up, stylizes them, and adds a captionβwhich customers can then download in a large format. Let's call the company Memories.The company started in the early 2000's, founded by an artist/photographer, and here's how it…
π₯ Explore this insightful post from Hacker News π π **Category**: π **What Youβll Learn**: We introduce PICO (Perceptual Image Codec) β the first learned codec that is both practical, and optimized directly for the human visual system. To derive it, we perform a comprehensive study of modeling choices for practical learned codecs, and search over millions of model configurations to jointly optimize over perceptual quality and on-device runtime. Based on large-scale subjective user studies, PICO provides 2.3-3Γ bitrate savings against AV1, AV2, VVC, ECM and JPEG-AI, and 20-40% bitrate savings against the best learned codec alternatives. At the same…
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**What Youβll Learn**: written on May 24, 2026 Pi is now part of Earendil, but in the important sense it is still Marioβs project. He has been living with its issue tracker longer than I have, and he has been exposed to the weirdness of the new form of agent traffic in Open Source projects for longer too. This post is mostly a reflection of my own experience after spending more time in the tracker, using Pi to work on Pi, and watching what I have learned about…
π Discover this trending post from Hacker News π π **Category**: π **What Youβll Learn**: Page introuvable... | Usborne | Des livres pour explorer le monde
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π₯ Read this insightful post from Hacker News π π **Category**: β
**What Youβll Learn**: For each AI chip designed by Nvidia, AMD, Google, and Amazon, we estimate the per-chip cost of four component categories: memory (HBM), logic dies, advanced packaging (CoWoS), and auxiliary components. We then multiply those per-chip costs by estimated quarterly production volumes to get total component spending in each category, and compute each categoryβs share of total component spending per quarter from Q1 2024 to Q4 2025. We find that memoryβs share rose from 52% to 63% over this period, while packaging fell from 19% to…
β¨ Check out this insightful post from Hacker News π π **Category**: β
**What Youβll Learn**: location_surface_grids = π₯ with location_error_gp_model: for multiplier, X_noisy_value in zip(multipliers, noisy_xs): pm.set_data(π¬) posterior_mean_point = π¬ f_mean, _ = gp_location.predict(Xnew, point=posterior_mean_point, diag=True, pred_noise=False) location_surface_grids[multiplier] = f_mean.reshape(n_prediction_grid, n_prediction_grid) naive_kde_grids = π¬ kde_bandwidths = { multiplier: location_error_idatas[multiplier].posterior["β"].mean(("chain", "draw")).item() for multiplier in multipliers } for multiplier, X_noisy_value in zip(multipliers, noisy_xs): squared_distance = ( (Xnew[:, 0, None] - X_noisy_value[:, 0]) ** 2 + (Xnew[:, 1, None] - X_noisy_value[:, 1]) ** 2 ) weights = np.exp(-0.5 * squared_distance / kde_bandwidths[multiplier]**2) naive_kde_grids[multiplier] = (weights @ y_walker / weights.sum(axis=1)).reshape(n_prediction_grid, n_prediction_grid) location_norm =…
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**What Youβll Learn**: Out of all the migrations I help teams with, Go to Rust is a bit of an outlier. Itβs not a question of βis Rust faster?β or βdoes Rust have types?β, Go already gets you most of the way there. The discussion is mostly about correctness guarantees, runtime tradeoffs, and developer ergonomics. A quick disclaimer before we start: this guide is heavily backend-focused. Backend services are where Go is strongest, small static binaries, a standard library focused on networking, and an ecosystem of libraries for…
