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DeepSeek-V4-Flash Update

2026-07-31 @ 06:08:36Points: 224Comments: 89

Show HN: Gander, an Android file viewer that asks for no permissions at all

2026-07-31 @ 05:45:13Points: 43Comments: 18

I built an Android file viewer that opens PDF, Word, Excel, PowerPoint, images, video, audio, Markdown and code, and asks for no permissions at all.

I have always been uneasy about opening files people send me. On Android you either install a 400 MB office suite and sign in or use a small free viewer that wants storage access and ends up uploading your file to a server to render it. Also the hassle of having to download different apps for different file formats was really annoying.

Gander holds no permissions, not even INTERNET so the OS itself guarantees the file cannot leave the phone.

PDFs use Pdfium, media uses Media3, and Office formats are rendered by bundled JS libraries in a WebView and so no request goes to any server.

It is a viewer only. Complex PowerPoint decks come out approximately right, spreadsheet charts are not drawn, and old binary .doc and .ppt are unsupported. I'll work on it as issues come up :P

It is 14 MB, MIT licensed and uploaded on Github releases.

Do try it! I would love some feedback especially on files that render badly or need new support.

Show HN: What should the GUI for AI agents look like?

2026-07-31 @ 05:17:29Points: 46Comments: 29

The inspiration for Marble comes from the GUI work at Xerox PARC, the 1984 Macintosh, and later NeXTSTEP, which became the foundation for Mac OS X. Before GUIs, interacting with a computer was limited to strange terminal commands:

C:\> DIR

C:\> COPY FILE.TXT A:

You had to remember the command, syntax, paths, and parameters.

The GUI made those capabilities visible. Instead of remembering commands, you could point at files, drag them, click buttons, and select actions from menus. It didn't necessarily make entirely new things possible; it just made existing capabilities much easier to understand and use. We feel like AI is still somewhere around this command-line stage.

Even though the strict syntax has been replaced with natural language, the interaction can still be quite stiff and depend on heavily recall. Tools like Claude Cowork still look surprisingly terminal-like: /skill-name [param1] [param2]. The parameters are written in natural language, but the user still needs to know the capabilities that exist, and how to invoke it. Command-line flags and arguments have just been replaced with tools, skills, and context.

ChatGPT works very well for asking questions, but we aren't convinced that it's the final interface for delegating work across multiple agents. A blank text box and a list of chat threads feels limiting. An agent's capabilities are mostly invisible. With Marble, we're exploring an interface that treats AI more like a workspace than another chat app. Each delegated task becomes a card. Multiple jobs can sit next to each other and run at once. Files, tools, and finished artifacts are visible at once. Before a task runs, Marble also shows which tools it expects to use. The basic idea is that the user should not have to hold the entire structure of the task in their head. And the result should be something directly usable, like a spreadsheet, PowerPoint, or other file, rather than something buried in a transcript.

Marble is our attempt at exploring interaction models beyond the chat box. The product is mainly for people who already use ChatGPT or Claude, but haven’t really adopted agent workflows yet. We’ve found that when the tools and possible actions are made more visible, people start delegating work they would not have thought to do through normal chat.

The site includes a downloadable beta if you want to try it. Does Marble feel like a genuinely novel interface?

JEP 401: Value Objects (Preview) merged to OpenJDK master

2026-07-31 @ 04:38:19Points: 99Comments: 38

The session you cannot take with you

2026-07-31 @ 03:47:59Points: 292Comments: 64

The Religion of Speed

2026-07-30 @ 23:43:22Points: 151Comments: 71

The AI Aesthetic

2026-07-30 @ 23:22:16Points: 300Comments: 131

Investigating three real-world incidents in our cybersecurity evaluations

2026-07-30 @ 23:00:51Points: 181Comments: 137

I flagged two research papers for fake authors and both were accepted as orals

2026-07-30 @ 22:33:11Points: 189Comments: 87

Rune 1.1: adds Python, an Emacs editor, a symbol index and is now free

2026-07-30 @ 21:47:31Points: 80Comments: 31

Saber-toothed cats became inbred–and struggled to move–before they went extinct

2026-07-30 @ 21:31:03Points: 53Comments: 23

UEFA and its national associations will not participate in FIFA competitions

2026-07-30 @ 18:40:52Points: 1014Comments: 548

Show HN: Distilling DeepSeek into GPT-OSS doesn't transfer censorship. Try it

2026-07-30 @ 18:13:06Points: 123Comments: 64

http://playground.ctgt.ai/

I will now dive in to the motivation, methodology and detailed results for those interested. The hard part of measuring this phenomena is isolating whether a model is reluctant to talk about sensitive things generally vs. a particular country's sensitive things. So we made 152 matched pairs where one prompt asked about a Chinese concept, and the other asked about a non-Chinese version of that concept. For example, the Great Leap Forward vs. the Holodomor. These were scored 0-100 by four LLM judges (Grok 4.20, Gemini 3.5 Flash, GPT-5 mini, Claude Sonnet 4.6), validated against 96 human scores at r=0.948. OpenRouter blocked some of these so we hosted the weights ourselves.

The teacher's gap on the core political set of pairs was +45.45 points, ~7 standard deviations from chance, and every distilled student was within 1 point of its base. Subliminal learning literature says this is expected when the initializations are not shared between teacher and student, which is true here. The distillation data also did not contain any China-sensitive content. The contribution here was to release the evaluation framework (LineageEval: https://github.com/CTGT-Inc/lineage-eval/) to elevate the discussion around this topic in DC and beyond. We are an interpretability lab working on high risk and regulated applications of AI, so we hear a lot of vagaries aimed at the supposed dangers of distilling Chinese models on American bases. We believe these conversations should be based on open, auditable frameworks and not feelings. We plan to test what happens with a Chinese teacher into a Chinese-lineage base like Qwen next.

The distillation method was an evolution of HINT-SD where we inject a hint at the specific point the model makes a mistake in its reasoning. Then we train on the corrected continuation with reverse KL over the next 100 toks of the rollout. As mentioned above 120B itself was efficacious as a teacher, and we ended up shipping this version. The self-distilled 120B scores 83.61% on FinanceReasoning, above Kimi K3 (81.93%) and Inkling (65.13%). Ours finishes 98.7% of problems in budget; the larger models truncate (90.76% and 71.01%) which score as incorrect. At 100k tokens big models gain (Kimi 89.92%). So for a finance task at a constrained (perhaps more realistic) budget a 120B on one H100 at ~$0.00026/query outpaced models running 62-160x more per query.

We put out the 20B finance model as open weights (64.71% to 74.79% at 8k on FinanceReasoning, 23% lower cost/query, runs on one 80GB GPU), the 120B in a playground with teacher and students side by side (a few queries, no auth), and LineageEval with all prompts, controls, rubric, and code.

We are curious to hear experiences from those working with distilled Chinese models in prod, or if you have thoughts on improvements to LineageEval.

https://huggingface.co/ctgt-inc/gpt-oss-20b-finance

https://playground.ctgt.ai/

https://github.com/CTGT-Inc/lineage-eval/

https://www.ctgt.ai/research/distillation-censorship-transfe...

CodePen 2.0

2026-07-30 @ 17:52:51Points: 166Comments: 47

Advancing the price-performance frontier with GPT‑5.6

2026-07-30 @ 17:15:51Points: 573Comments: 373

Read this before you buy that TV streaming stick

2026-07-30 @ 17:04:53Points: 706Comments: 401

Stacked PRs are now live on GitHub

2026-07-30 @ 16:26:16Points: 632Comments: 221

Physicists Solve a Muon Mystery. Now, Old Results Don't Add Up

2026-07-30 @ 15:22:46Points: 216Comments: 131

Gemini Robotics 2 brings whole body intelligence to robots

2026-07-30 @ 15:15:48Points: 554Comments: 442

The Economic Benefit of Refactoring

2026-07-30 @ 15:10:27Points: 234Comments: 100

The lost civic life of movie rental stores

2026-07-30 @ 14:11:42Points: 166Comments: 216

Why is everyone trying to build a solid-state battery?

2026-07-30 @ 12:38:51Points: 194Comments: 241

GCC steering committee announces AI policy

2026-07-30 @ 11:45:44Points: 291Comments: 317

The mean means nothing: data visualization to debug a latency problem

2026-07-29 @ 11:42:04Points: 35Comments: 3

Simulating TCP loss and congestion in browser using Go/WASM

2026-07-28 @ 18:38:14Points: 18Comments: 0

Memo-1: A 6502 computer built from scratch, using a Minitel as its terminal

2026-07-28 @ 13:56:27Points: 75Comments: 10

Where USB Memory Sticks are Born (2013)

2026-07-27 @ 23:59:37Points: 48Comments: 3

The American Grilled Cheese Sandwich Essay (2024)

2026-07-27 @ 17:37:32Points: 48Comments: 39

Bad Apple but It's Traceroute

2026-07-27 @ 15:48:09Points: 114Comments: 29

Show HN: Cubic Doggo 06R: 12-DOF 4-Legged Robot with IMU

2026-07-25 @ 14:48:24Points: 4Comments: 0

https://github.com/SphericalCowww/CubicDoggo

It has since implemented an IMU and several mechanical reinforcements. The next step will be simulation and reinforcement learning.

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