The Tech Panda takes a look at recent launches in the superfast field of Artificial Intelligence (AI).
Crypto: AI Trading Avatars based on Seven Trading Strategies
Bitget, the Universal Exchange (UEX), launched six all-new AI trading avatars inside GetAgent, marking a major step toward next-generation smart trading. As traders across the world test how well leading AI models like ChatGPT, Gemini and DeepSeek perform when placed into real trading environments, Bitget offers something more tangible: a lineup of AI traders with distinct personalities, strategies, and market philosophies, all running live, trading real accounts, and fully available for one-click copy trading.
“People want solutions they can actually use to trade,” said Gracy Chen, CEO of Bitget. “These avatars make trading feel more personal and more approachable. Whether someone prefers momentum, hedging, or contrarian plays, there’s now an AI companion that thinks the way they do.”
Features
- Each avatar represents a different school of trading logic, ranging from conservative hedging and major-coin momentum to high-beta altcoin breakouts, contrarian reversals and mechanical execution. These include Steady Hedge, Majors Momentum, Altcoin Turbo, CTA Force, Infinite Grid, Dip Sniper, and DeepSeek (base model).
- All strategies have been built using a multi-factor library of professional trading indicators, extensive backtesting, and iterative refinement inside GetAgent.
- Till 18:00 on December 15 (UTC+8), GetAgent users can access a limited one-click copy trading channel, selecting the avatar that aligns most closely with their trading personality.
- Each AI trader executes autonomously in real time, and users can follow every entry, exit, drawdown and adjustment directly in the Model Arena.
Cloud: Unified Data Access & Governance with AI-Powered Federation & Lineage
Cloudera, the company bringing AI to data anywhere, announced a major platform update that integrates Trino, Cloudera Shared Data Experience (SDX), and Cloudera Octopai Data Lineage to deliver unified data access, control, smarter governance, and lineage across the entire data estate. With the power of AI-driven automation and intelligence, enterprises can now seamlessly discover, govern, and access all their data, wherever it resides.
“Our mission at Cloudera has always been to empower enterprises to make trusted data available for every AI initiative,” said Leo Brunnick, Chief Product Officer, Cloudera. “With this release, we’re taking a major step forward, bringing AI-powered automation, governance, and access together under one platform. Enterprises can now securely harness all their data anywhere to accelerate innovation and drive better business outcomes.”
Features
Through this AI-infused architecture, Cloudera enables enterprises to:
- Boost efficiency through automation of data fabric operations such as cleansing and classification
- Democratize access with natural language interfaces for intuitive data interaction
- Improve trust and transparency through intelligent metadata collection and end-to-end data lineage
Entertainment: Mapping the Hive Mind of Fan Predictions for Stranger Things Final Season
What if data could predict the end of your favorite show? Neo4j, the world’s leading graph intelligence platform, has launched HopperGraph: an interactive, AI-powered visualization that dives into the internet’s most popular “Stranger Things” fan theories to forecast what might unfold in the hit series’ fifth and final season. For example, will Eleven read Henry Creel’s mind to save Hawkins?
“Stranger Things is built on unknown connections between characters, timelines, and worlds, and that’s what Neo4j specializes in,” said Stephen Chin, Vice President of Developer Relations at Neo4j. “With HopperGraph, we wanted to show how graph database and analytics technology can reveal hidden relationships between ideas, people, and possibilities – from the Upside Down to the everyday.”
Features
- Drawing from 150,000 Reddit posts, 234,000 nodes, and 1.5 million relationships, the analysis taps into the internet’s most active hub of Stranger Things speculation, where theories emerge, evolve, and gain traction long before each season airs.
- Fans can explore HopperGraph’s interactive microsite at strangergraphs.com, where AI agents modeled after the show’s beloved characters guide users through theories, relationships, and plot possibilities hidden within the data.
- HopperGraph is built on Neo4j AuraDB and applies natural language processing and community detection algorithms to identify which fan communities have historically predicted storylines most accurately. The analysis maps recurring themes and cross-season prediction patterns to reveal clusters of fans whose past theories turned out to be right more often than chance.
- HopperGraph’s AI “Stranger Agents”, powered by GraphRAG (Graph Retrieval-Augmented Generation) for greater accuracy, also uses the show’s character backstories as a data lens, connecting narrative arcs and emotional cues to visualize predictions through the personalities of Eleven, Max, and others.
CRM: The First Conversational Intelligence Built with Revenue Context
MaxIQ, the first AI-powered Revenue Journey Platform that connects every stage of the customer lifecycle, launched EchoIQ, a breakthrough conversational intelligence engine built on complete revenue context and the first to abandon per-seat licensing. The milestone reflects rising demand for intelligent systems that unite sales, success, and operations in one connected platform.
“Enterprises don’t need more dashboards; they need systems that act,” said Sonny Aulakh, Founder of MaxIQ. “EchoIQ is powered by Agentic AI, which means every conversation becomes part of a connected revenue story. Traditional CI tells you what was said. EchoIQ tells you what it means for your number and what to do about it.”
Features
- Instead of logging meetings or analyzing isolated calls, EchoIQ captures conversations across channels including meetings, emails, and product usage, and translates them into insights that update deal health, forecast accuracy, and retention models in real time.
- EchoIQ builds on the success of InspectIQ and ForecastIQ, MaxIQ’s first AI engines that reimagined deal inspection and forecasting.
- The upcoming SuccessIQ module will complete the lifecycle by connecting post-sales adoption and growth.
- Together, these Agentic AI engines replace static CRM dashboards with an adaptive system that learns from every interaction and acts across the entire customer journey.
Enterprise software: A new state-of-the-art Tabular Foundation Model transforming how Enterprises harness AI for Structured Data
Lexsi Labs (formerly AryaXAI Alignment Labs), by Aurionpro, the research lab dedicated to frontier research in AI Alignment and Interpretability, launched Orion-MSP, a new state-of-the-art (SOTA) foundation model to deliver enterprise-grade accuracy and scalable deployment for any tabular predictive task.
“All current AI research is primarily aiming to predict the world in ‘zero-shot’. We are seeing this achieved by modality-specific SOTA models like LLMs for text, LRMs for reasoning tasks, and LVMs for vision tasks. We want to make this possible as well for tabular predictive tasks with Orion-MSP class of models. Orion-MSP is the state-of-the-art model, with top mean-rank accuracy across various benchmarks compared to other TFMs, including classic Machine Learning models,” says Vinay Kumar, Founder & CEO of Arya.ai and Founder of Lexsi Labs. “We are also launching TabTune, which is the perfect tool for any practitioner or enterprise to infer or fine-tune TFMs. With advanced components like fairness and conformity index, this is purpose-built for enterprises. We are releasing these classes of models under the MIT license, making both the models and the tool fully usable without constraints.”
Features
- Enterprises such as banks, insurers, and healthcare organizations generate large volumes of structured, table-based data from transactions, customer records, insurance claims, health records, sensor telemetry, and ledgers.
- Lexsi Labs’ latest models allow organizations to predict any task in zero-shot, on such enterprise data, using a single model through an API or improve it further through fine-tuning to achieve the best performance in just a few lines of code. This transforms the current data science from multi-pipeline processes into a zero-shot or few- step deployment.
