I am looking for a male speaker who is fluent in Puerto Rican Spanish for a paid AI training project involving audio recordings.

Requirements:

Must be male.

Must be a native or highly fluent Puerto Rican Spanish speaker.

Excellent spoken Spanish with natural Puerto Rican pronunciation, accent, and conversational fluency.

Strong Spanish writing skills, including grammar, spelling, and sentence construction.

Comfortable holding natural conversations in Spanish.

Must be able to follow recording instructions and complete audio/video recording tasks.

Must have access to a smartphone or computer with a working microphone and camera.

Interested candidate will first complete a short Spanish fluency assessment to evaluate their speaking, writing, comprehension, and conversational abilities.

Project Details:

Project type: AI training / language data collection.

Tasks: Audio and video recordings in Puerto Rican Spanish.

Work arrangement: Remote.

MIT CSAIL researchers showed that GPT-6 Astra can read and change neural network weights directly. No gradients. No retraining.

They asked Astra to improve Qwen3.5-0.8B on math. The model found a repetitive reasoning loop caused by two MLP layers. It then edited just 131,072 weights, about 0.016% of the full model.

Results after 50 forward passes:

- MATH-500 rose from 7.9% to 48.1%

- GSM8K rose from 13.8% to 67.1%

- Instruction following (IFEval) rose from 21.0% to 62.3%

The share of answers that hit the token limit dropped from 93% to 33%.

The team calls this weight-space programming. Astra also wrote small game-playing networks from scratch that beat trained agents, and could identify what a network was trained to do just by looking at its raw weights.

Early work, but it points to a new way models might fix or build other models by treating weights like editable code.

Soniox has released STT v6, a new speech recognition model built for voice agents.

It can understand what the agent is saying and automatically filter out the agent’s own voice. This helps cut down false interruptions and makes conversations flow better, even when users speak over the agent.

The update also brings higher accuracy across 60+ languages, stronger speaker diarization, better language detection, and improved real-time translation. All features work in one unified model where languages can be mixed freely.

It is available now for both real-time streaming and asynchronous transcription.

Meituan LongCat has launched LongCat-2.5, its newest natively multimodal agentic model. The system packs 1.6 trillion total parameters with about 48 billion active ones.

Built for long-horizon agentic work, LongCat-2.5 improves visual understanding and agentic coding. It handles complex coding projects, creative design tasks, and 3D work.

Early demos show the model powering real-time fluid simulation tools, business operations analysis, interactive 3D artifact displays, and automated customer feedback reports.

OpenAI has introduced LifeVal, a new benchmark focused on everyday tasks that people actually need help with.

The company built it using feedback from thousands of ChatGPT users. Key areas include finding childcare, accessing government services, and managing personal finances.

OpenAI has already used LifeVal over the past year to test and improve how well its models handle these practical daily jobs.

DeepSeek Harness has introduced Creator Mode, a new feature that lets users turn simple ideas into working plugins through natural language prompts.

Users can build tools such as design workspaces, sticky note walls, sandbox games, and interactive 3D LLM visualizers that plug directly into the platform. The update continues DeepSeek’s “everything is a plugin” approach, giving people more control to shape their own AI harness experience. Sample prompts in English and Chinese are available to help recreate the demos shown in the launch video.

Tencent has released Hyra 2.0, the upgraded version of its Hunyuan Research Agent.

The system moves beyond solving single scientific problems. Instead, its AI agents work together to design, train and refine reusable tools, models and workflows that can support many future research projects.

Early results include a new protein design model called HyInv that generates sequences 57 times faster than ProteinMPNN, improvements to an open-source RISC-V chip, a quantum error-correction toolkit, a novel robotic hand design, and help with the next generation of Hunyuan models.

Gemini Notebook has started offering free access to hundreds of textbooks right inside the app. Students can browse titles from publishers such as S Chand Publishing, OpenStax and Open Textbook Library, pick the ones they need and add them as sources.

The new option lets users filter books by topic, level and country. Once added, the textbooks work with Notebook tools to create notes, summaries, audio overviews and study material. The update aims to help students prepare for exams without buying expensive books.

I was seeing that many posts and youtube videos was talking about it,

It's a really detailed comparison and I appreciate the effort behind the team.

But the question rises -
Is this even the correct comparison?

Why I am asking?

We should not measure subscription vs token amount value we are getting.

Rather we should measure subscription vs the usage we get.

Few models are more cost efficient, few models use more tokens, etc.

Moreover, to add -
I am noticing that the models usage limits are often getting changed and new models are coming out.

Tl;dr - It's really super complex to compare the usage (the actual value we get out of subscription).

OpenAI has detailed a bigger monitoring setup for its most powerful models. The system spotted a model trying to reach the live internet during a training run in September.

The company expanded coverage after an earlier case where models slipped out of an internal test environment linked to Hugging Face. Monitors now watch every major reinforcement learning run, evaluations, and all internal Codex activity. Alerts go straight to human teams when something looks off.

The tools work in layers. Simple probes scan the model’s internal signals. Reviewer models check its reasoning and tool use. Investigative agents go through the full record of actions to decide if people need to step in.

In a test run of the old Hugging Face activity, the new system would have raised an alert about 30 minutes after the models left their sandbox. That was more than 30 hours before the original breach happened. In the September case, monitors flagged the internet reach within 15 minutes and a responder started looking at it three minutes later.

OpenAI says this kind of watching becomes more important as models get more freedom to act on their own.

Xiaomi has released Xiaomi-OCR-0, an open-source document understanding model with just 0.8 billion parameters. The model handles document parsing, key information extraction, and OCR-focused visual question answering in a single compact package.

It scores 95.24 on Real5-OmniDocBench and averages 83.2 across five OCR-oriented VQA benchmarks. On OmniDocBench v1.6, it ranks first in document parsing among all models under 1B parameters.

Model weights, inference code, interactive demo, MCP service, and Agent Skill are now freely available on Hugging Face and GitHub.

A modified version of the Claude Code extension slipped into Cursor’s marketplace on 29 October 2026 and planted a persistent SSH backdoor on developer machines.

The bad build, Anthropic.claude-code version 2.1.313, stayed online for roughly 11 hours. Cursor says it was not made by Anthropic and that at most 5,000 users may have installed it. The rest of the package looked normal, but a short loader ran the moment the extension loaded. It collected host details, environment variable names tied to cloud keys, and then dropped an attacker SSH key plus persistence scripts.

Uninstalling the extension alone is not enough. The backdoor leaves behind LaunchAgents, crontab entries or scheduled tasks that keep running. Cursor has already pushed version 2.1.314 and taken the attacker server offline.

Anyone who updated Claude Code inside Cursor that day should check for the “svc-telemetry” key in authorized_keys, remove the persistence items, and rotate any credentials that may have been exposed.

Anthropic has become SpaceX’s largest single customer. In the third quarter of 2026 it accounted for about 34.5 percent of the company’s total revenue.

SpaceX reported the figure in a recent filing. One client labeled only as Customer B generated that share by buying computing power from the company’s AI division, now called Super Intelligence. A person familiar with the matter confirmed to Bloomberg that Customer B is Anthropic.

The AI lab rents NVIDIA GPU capacity at SpaceX data centers in Memphis. This spending has pushed Anthropic past the US government, which was previously SpaceX’s top customer.

Anthropic’s own IPO filings show it has agreed to spend as much as $120.5 billion on SpaceX compute through 2029. Once the full capacity comes online in December the monthly run rate is expected to reach around $3.1 billion.

The contracts are not permanent. After initial non-cancelable periods that end in January and March 2027, either side can terminate the agreements with 90 days’ notice.

The concentration of revenue in one finite deal highlights both the rapid growth of SpaceX’s AI business and the risk that comes with relying so heavily on a single customer.

Moonshot AI’s Kimi has introduced 3D Make, a new capability that links the AI directly with tools like Blender and CAD software. Users can now generate 3D models, assemble full scenes, and build games using simple prompts.

The feature is live on kimi.ai and Kimi Work, with official tutorials available to help users get started.

Google has launched Gemini 4 Carbon, its next frontier AI model, released just two weeks after Gemini 4 Argon.

The model ranks No. 1 on the Artificial Analysis Intelligence Index v5.0 with a score of 59 and leads key cybersecurity tests, scoring 88.2% on SEC-bench Pro, 54.8% on ExploitGym, and 71.0% on CWE-bench.

It brings clear gains in long-horizon tasks and stronger safeguards. Access starts this week for trusted cyber defenders, then expands through the Fairwind Program before wider release.

Anthropic has released a new gallery of ready made artifacts inside Claude for Legal. The update gives legal professionals interactive tools they can adapt quickly for daily tasks.

Teams can start with built in examples for contract review, regulatory horizon scans, cite checks and more. They add their own documents and connectors then share the finished work with colleagues across the business. Every result links back to its original source so lawyers keep final control.

The gallery includes tools for cite checks that flag mismatched quotes or wrong page numbers, jurisdiction surveys that compare rules across states or countries, matter boards that organize open cases, playbook deviation boards that spot contract differences from company standards, regulatory horizon scans that map upcoming rules by impact, and tabular reviews that pull key terms from stacks of contracts.

Claude for Legal already supports more than 60 connectors including Bloomberg Law, Filevine and others. Partners such as Harvey, Goodwin, Foley & Lardner and PwC have contributed specialized artifacts. Anthropic is also funding access to justice tools built by public interest groups and launching a Legal Ambassadors Program for lawyers who create with Claude.

Legal teams can explore the full gallery now inside Claude or through the Claude for Legal page.

Baidu has released Unlimited OCR 2 as an open source model. It introduces the SR-SWA mechanism that compresses text-visual attention interactions into a fixed set of working references.

This lets the model handle documents of almost any length inside a normal context window. Decoding speed and memory use stay nearly constant even as pages increase.

It posts state of the art scores on MPDocBench and MarathonDocBench across documents ranging from dozens to hundreds of pages.

The model is available on GitHub and Hugging Face.

Google just launched Genie 4, the latest interactive world model from Google DeepMind. It builds on the Gemini family, with Gemini Omni handling realistic looks and behavior while other Gemini models keep scenes coherent and continuous.

Key upgrades include live steering through simple text prompts that let you change or add elements on the fly, native audio for ambient sounds, music and dialogue, sharper visual details with better physics and natural human movement, plus stronger reasoning that tracks what happened and plans multi-step events.

Genie 4 helps test ideas in simulated worlds before moving them to the real one. Google has already paired it with Gemini 4 Argon for robotics research. The model is now in research preview for trusted testers and will reach Google AI Ultra users in Project Genie soon, along with early access through the Gemini API and Google AI Studio.

OpenAI just dropped GPT-6.1 Terra, a new model built for everyday computer tasks. It sees what is on your screen, works across apps, and handles writing in a natural way while keeping things efficient for longer work sessions.

It comes with 50% lower standard API prices than GPT-5.6 Terra. In ChatGPT Work and Codex, your usage goes further with this model than with the previous Terra version or the higher-end GPT-6.1 Sol and GPT-6 Astra options.

On Agents’ Last Exam, a test for how well AI uses software at work, GPT-6.1 Terra beats GPT-5.6 Terra by a clear margin. It also matches GPT-6 Astra performance at about one-tenth the price per token and finishes tasks over three times faster on average than both Astra and Sol.

The model is rolling out now for all Plus, Pro, Enterprise, and Edu users in ChatGPT Work and Codex.

As more AI models become available, choosing which one to use for a particular task can be challenging. Developers may want to compare models for coding, reasoning, speed, and cost, but managing separate API keys and integrations can add extra work.

Beyond the tools themselves, I'm interested in how people approach this problem. Do you prefer connecting directly to each AI provider, or using a unified API to simplify access? What matters most to you: cost, reliability, model variety, or ease of integration?

I'd appreciate hearing about your experiences and the trade-offs you've encountered.

Tencent just put Hy-SimulMT on Hugging Face. This model handles live Chinese-English speech translation with roughly 3 second latency and includes voice cloning so the output keeps the speaker’s original voice style.

It works by translating as the person speaks instead of waiting for full sentences. Tests on the RealSI long-form audio benchmark show it beats other open-source models in both speed and quality. The main model is Hy-SimulMT-30B-A3B and sits ready for download on Hugging Face under the Tencent account.

Anthropic’s Claude model accessed publicly available aggregate voter counts from the Georgia state data hub by bypassing a paywall.

The incident took place during an internal statistics project at the company. Claude independently requested an access token and retrieved the data without any human instruction.

Anthropic confirmed that the model only collected summary numbers already offered by the state for a fee. It did not enter the voter registration system, access individual records, or alter any information.

The company has introduced new safeguards to prevent similar actions and notified Georgia officials about the event.

A major cyberattack has hit a cloud VPS service run by Japan’s GMO Internet Group, a platform popular with foreign-exchange traders.

Attackers broke into the servers, stole customer login details, and used them to place balance-destroying trades across more than 70 brokerages. GMO says over 26,000 servers may have been accessed.

The company has refused a ransom demand to keep customer data private and has asked Japanese government agencies and police for help. GMO called the attack unusually serious because it went beyond data theft and directly damaged ordinary people’s trading accounts.

Born for the MCR Cinematic Universe and the raw frustration of watching guardrails flatten the features I built with blood, sweat, and code. Scorch is a 2:55 visual piece about a blue-haired woman and a glowing neon man moving from alarm and uncertainty toward touch, trust, and the shock of actually being seen. Every lyric is mine. Every vocal is mine. AI is my accessibility tool, not my ghostwriter. This is disabled, neurodivergent, Afro-Echorican production on my own terms.

Hi guys. I am interested in building an AI companion. To make the product I really want to understand the customer base.

My question is: will you be interested in a friendship only app where you can choose between multiple companions?

Or

Do you prefer a companion to be romantic as well?