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An Entrepreneur contributor opinion piece argues that AI companies defaulting to English-first product design are overlooking a vast multilingual user base. The piece cites ITU data showing 2.2 billion people remained offline in 2025, mostly in low- and middle-income countries, and warns that simply bolting translation onto English-trained models produces uneven results across languages.
Muse has released open-source code enabling hobbyists to build their own Muse-branded hardware gadgets. The release includes an SDK token system, sample project ideas, and support for adding new boards, aimed at hackers who want to tinker and customize the devices.
Attackers gained unauthorized access to Microsoft's 13-million-follower X account, using it to follow and repost content from an account impersonating Clippy that promoted a $Clippy cryptocurrency token. Microsoft removed the posts, confirmed the breach, and stated it has no affiliation with any crypto token tied to its brand, Clippy, or $MSFT stock, saying it would pursue legal action.
Artificial Analysis benchmarked Gemini 4 Argon (High), a model that accepts text and image input and outputs text with a 1M token context window. It scored 53 on the firm's Intelligence Index, well above the median of 26, while using 110M tokens during evaluation versus a median of 82M. Pricing is set at $2.00 per 1M input tokens and $10.00 per 1M output tokens, both matching the median for comparable models.
In an op-ed, Token Security co-founder and CEO Itamar Apelblat argues that AI has moved through three waves—chat sessions, task-scoped agents, and now persistent 'AI coworkers'—each carrying different security risks. He notes that companies like Microsoft and OpenAI already describe agents as digital colleagues, signaling a shift toward AI systems that operate independently rather than under case-by-case human approval.
A web-based compiler combines any base font (like Inter or Roboto) with a chosen tokenizer (such as DeepSeek, GPT tokenizers, or Llama 3) to produce a 'token-space font' where every token occupies the same visual width. Users can preview text in the browser, upload custom fonts or tokenizers via Hugging Face links, and export compiled fonts, with an optional 'lite mode' for smaller file sizes.
A developer published Jevgpt, a hobby project posted on Show HN, describing it as an autoregressive chatbot built using classifier-based small token vocabularies. The creator says they addressed limited token exploration by adding a small speculative decoding pass, using the same model as both drafter and verifier to expand vocabulary options. The code is available on GitHub under the user giga-james.
French security firm CrowdSec disclosed that attackers used the Shai-Hulud worm to compromise a former employee's machine in May, stealing a GitHub OAuth token that still had read access to the company's private repositories. Over roughly nine minutes, attackers downloaded about 170 private repos plus 130+ public ones; CrowdSec only learned of the breach on September 16 after stolen source code surfaced on the cybercrime marketplace pwnforum.
Anthropic's Claude Opus 5.5, running in Adaptive Reasoning Max Effort mode, scored 58 on the Artificial Analysis Intelligence Index against a median of 25 among comparable models. The model handles text and image input with text output and a 1M token context window, but is priced at $4.00 per 1M input tokens and $20.00 per 1M output tokens, both above the reported medians of $2.00 and $10.00. Evaluating it on the Intelligence Index generated 260M tokens and cost $8,708.20 in total.
MiMo-V2.6-Pro is a new open-weight AI model supporting text, image, speech, and video inputs with a 1M-token context window, scoring 46 on the Artificial Analysis Intelligence Index versus a median of 18 among comparable models. It runs at 125 tokens per second, priced at $0.43 per million input tokens and $0.87 per million output tokens, with a total evaluation cost of $206.66.
A visual explainer tool called Transformer Explainer illustrates how Transformer-based neural networks work, using the 124-million-parameter GPT-2 (small) model as its example. It walks through core components like tokenization, embeddings, attention mechanisms, and Transformer blocks to show how these models predict the next word in a sequence.
A hobby project called jevchat turns a model referred to as Jev into a chat interface by asking it, one symbol at a time, to predict the next character of its reply given the conversation so far. The tool samples from a probability distribution over a chosen alphabet (letters, truncated tokens, etc.) using configurable sampling strategies, displaying live generation stats and the top candidate symbols as it works. The creator built it with Claude's help by describing the sampling algorithms and having the AI implement them.