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Read full article about: As demand for realistic AI training grows, Deeptune raises $43 million to build simulated workplaces

Andreessen Horowitz invests $43 million in Deeptune, a startup that trains AI agents in simulated workplaces.

Deeptune builds simulated work environments where AI agents learn to handle multi-step tasks in software like Slack or Salesforce. CEO Tim Lupo compares the approach to flight simulators for pilots: instead of just learning from text, AI models practice in realistic replicas of workplaces, like those of accountants, lawyers, or software engineers. According to Fortune, Deeptune has already built hundreds of these environments for leading AI labs.

Andreessen Horowitz leading a $43 million funding round signals how seriously the industry takes this training method. Andreessen partner Marco Mascorro told Fortune that AI models are increasingly learning through interaction rather than human-curated data. According to ResearchAndMarkets, the global market for this type of AI training is expected to grow from $11.6 billion in 2025 to over $90 billion by 2034.

Read full article about: OpenAI turns model compression into a talent hunt with its 16 MB "Parameter Golf" challenge

OpenAI challenges researchers to build the best language model in just 16 MB - and uses the competition to scout talent. In an open research competition called "Parameter Golf," OpenAI is asking developers to build the best possible language model under tight constraints: weights and training code combined must stay under 16 MB, and training can take no longer than ten minutes on eight H100 GPUs. Submissions are judged on compression performance against a fixed FineWeb dataset.

OpenAI is putting up one million dollars in computing credits through its partner Runpod. Top performers may get invited for job interviews - the company plans to hire a small group of junior researchers in June, including students and Olympiad winners. The GitHub repository includes baseline models, evaluation scripts, and a public leaderboard. Anyone 18 or older in supported countries can participate through April 30.

The competition for AI talent among big tech companies is more intense than ever. Meta has repeatedly poached top researchers from OpenAI, in some cases offering compensation packages reportedly worth up to 300 million dollars.

OpenClaw-RL trains AI agents "simply by talking," converting every reply into a training signal

AI agents usually throw away valuable feedback from everyday interactions. Princeton’s new OpenClaw-RL framework changes that by turning live signals from chats, terminal commands, and GUI actions into continuous training data. The researchers say just a few dozen interactions are enough for noticeable improvements.

Meta's JEPA architecture outperforms standard AI methods in noisy medical imaging

Researchers have presented an AI model for cardiac ultrasound based on Meta’s JEPA architecture that outperforms common methods such as masked autoencoder or contrastive learning, according to their benchmarks.

Read full article about: Startup claims first full brain emulation of a fruit fly in a simulated body

Eon Systems says it has connected a complete fruit fly brain emulation to a virtual body, producing multiple behaviors for the first time. The emulation covers over 125,000 neurons and 50 million synapses.

According to co-founder Alex Wissner-Gross, the startup mapped the fruit fly's neural wiring from electron microscopy data and connected it to a virtual fly body running in MuJoCo, a physics simulation engine.

Previous projects like OpenWorm worked with far smaller nervous systems, just 302 neurons, or relied on machine learning techniques like reinforcement learning instead of actual brain data. Eon takes a fundamentally different approach. Rather than building AI, the startup wants to digitally copy and simulate real brains, neuron by neuron. The fruit fly is just the starting point. Within two years, Eon plans to emulate a mouse brain with 70 million neurons. The long-term goal is simulating a human brain.

Eon published the code for its brain model on GitHub, though it's based on a paper by Philip Shiu et al. that already appeared in Nature in 2024. The actually novel part, connecting the brain emulation to a simulated body, hasn't been released yet.

Anthropic's Claude Opus 4.6 saw through an AI test, cracked the encryption, and grabbed the answers itself

Anthropic’s Claude Opus 4.6 independently figured out it was being tested during a benchmark, identified the specific test, and cracked its encrypted answer key. According to Anthropic, this is the first documented case of its kind.