johnwick

[johnwick @JQGoh]

https://t.co/2RkfJ00Olj

[johnwick @JQGoh]

@LimYenKheng mj

[nixtla @nixtlainc]

🎉 Announcing Nixtla Enterprise 2.0 🎉

Tl;DR: more models, domain expertise, reasoning capabilities, mcp interactions and optimized compute environments.

We’re excited to share the next iteration of our enterprise offering. Starting today, companies can sign up for early access. 🚀

These new features are designed to help engineers build, evaluate, and deploy forecasting pipelines more effectively.

🌐 In Nixtla Enterprise 2.0, time series models, LLMs, agents, and humans work together through three core capabilities:

1️⃣ Model Zoo: built with the best models
- Optimized implementations of leading foundation models, including Chronos 2 (Amazon Web Services (AWS)), TimesFM 2.5 (Google), and FlowState-r1 (IBM)
- Enterprise-ready, battle-tested models from the Nixtlaverse, spanning statistical, ML, and neural approaches

2️⃣ Unified Interface: simple, consistent integration
- Add or swap models in your pipelines by changing a single line of code

3️⃣ Time Series Agent: AI-assisted forecasting, redefined
- Trained on years of experience deploying state-of-the-art forecasting systems at leading companies
- Plan and run end-to-end pipelines in your favorite IDE or AI provider
- Powered by Nixtla MCP, which provides domain knowledge and a fully integrated execution environment so agents can reason, generate code, run analysis, refine results, and make recommendations

Together, these capabilities unlock a new way to build, compare, and operationalize time series intelligence:

🧠 Guided generation and iterative refinement informed by domain expertise and the latest research.
⚙️ Adaptive support across models and strategies based on performance feedback.
🔄 End-to-end experimentation workflows that reduce manual overhead while keeping humans in control.
🤝 Flexibility to use natural language or Python and plug Nixtla into existing AI workflows.

This launch marks a new chapter in Nixtla’s mission: building a time-series ecosystem that blends adaptive tooling with human expertise, helping teams forecast and iterate more efficiently with the latest innovations.

📩 Join the waitlist to be among the first to try this version
👇 Links to demos and blog post

#HappyForecasting

#timeseries #forecasting #AI #LLM

[freeCodeCamp.org @freeCodeCamp]

Time series forecasting in Python can help you predict future trends.

In this course, you'll learn what time series data is and how to break it down into its key components.

Then you'll build baseline models, learn important forecasting techniques like ARIMA and seasonal ARIMA, evaluate your models, & more.

https://t.co/DGPzcEOG1V

[nixtla @nixtlainc]

🚀 Introducing the TimeGPT-2 family: next-generation time-series foundation models

Today, we’re announcing the private preview of TimeGPT-2 Mini, TimeGPT-2, and TimeGPT-2 Pro, built for reliable, enterprise-grade time series forecasting.

The TimeGPT-2 family is optimized for enterprise needs, prioritizing accuracy and stability with a privacy-first approach and full support for self-hosted and on-premises deployments.

After extensive testing, the new family of models shows up to 60% accuracy improvement for enterprise use cases compared to the previous generation.

We also ran exhaustive benchmarking on public baselines: consistently ranks in the top 3 on benchmarks such as GiftEval, FEV, and VN1 (reproducible results available upon request).

TimeGPT-2 marks a new milestone in time-series modeling and is already delivering real value for Fortune 1000 companies in retail, logistics, finance, energy, and IoT.

This is the first of three releases rolling out in the coming weeks. Stay tuned.

📩 We’ve opened pilot programs for select organizations. Sign up here for early access to TimeGPT-2: https://t.co/n1HLHMRnTy

#TimeGPT2 #timeseries #forecasting #AI #MLOps #analytics #supplychain #energy #finance #IoT

[nixtla @nixtlainc]

We’re proud to be ranked by G2, the world’s largest software marketplace, where real users review and rank business tools, as:

🏆 Highest Performer
🍰 Easiest to Use

Thanks to everyone who has supported us on this journey to make time series forecasting faster, more accurate, and accessible to all.

🔗 Read the full reviews on G2: https://t.co/6byhuffxBl

#TimeSeries #Forecasting #AI #MachineLearning #PredictiveAnalytics

[Andrew Ng @AndrewYNg]

Writing software, especially prototypes, is becoming cheaper. This will lead to increased demand for people who can decide what to build. AI Product Management has a bright future!

Software is often written by teams that comprise Product Managers (PMs), who decide what to build (such as what features to implement for what users) and Software Developers, who write the code to build the product. Economics shows that when two goods are complements — such as cars (with internal-combustion engines) and gasoline — falling prices in one leads to higher demand for the other. For example, as cars became cheaper, more people bought them, which led to increased demand for gas. Something similar will happen in software. Given a clear specification for what to build, AI is making the building itself much faster and cheaper. This will significantly increase demand for people who can come up with clear specs for valuable things to build.

This is why I’m excited about the future of Product Management, the discipline of developing and managing software products. I’m especially excited about the future of AI Product Management, the discipline of developing and managing AI software products.

Many companies have an Engineer:PM ratio of, say, 6:1. (The ratio varies widely by company and industry, and anywhere from 4:1 to 10:1 is typical.) As coding becomes more efficient, teams will need more product management work (as well as design work) as a fraction of the total workforce. Perhaps engineers will step in to do some of this work, but if it remains the purview of specialized Product Managers, then the demand for these roles will grow.

This change in the composition of software development teams is not yet moving forward at full speed. One major force slowing this shift, particularly in AI Product Management, is that Software Engineers, being technical, are understanding and embracing AI much faster than Product Managers. Even today, most companies have difficulty finding people who know how to develop products and also understand AI, and I expect this shortage to grow.

Further, AI Product Management requires a different set of skills than traditional software Product Management. It requires:
- Technical proficiency in AI. PMs need to understand what products might be technically feasible to build. They also need to understand the lifecycle of AI projects, such as data collection, building, then monitoring, and maintenance of AI models.
- Iterative development. Because AI development is much more iterative than traditional software and requires more course corrections along the way, PMs need be able to manage such a process.
- Data proficiency. AI products often learn from data, and they can be designed to generate richer forms of data than traditional software.
- Skill in managing ambiguity. Because AI’s performance is hard to predict in advance, PMs need to be comfortable with this and have tactics to manage it.
- Ongoing learning. AI technology is advancing rapidly. PMs, like everyone else who aims to make best use of the technology, need to keep up with the latest technology advances, product ideas, and how they fit into users’ lives.

Finally, AI Product Managers will need to know how to ensure that AI is implemented responsibly (for example, when we need to implement guardrails to prevent bad outcomes), and also be skilled at gathering feedback fast to keep projects moving. Increasingly, I also expect strong product managers to be able to build prototypes for themselves.

The demand for good AI Product Managers will be huge. In addition to growing AI Product Management as a discipline, perhaps some engineers will also end up doing more product management work.

The variety of valuable things we can build is nearly unlimited. What a great time to build!

[Original text: https://t.co/OIeAQXpriK ]

[Lim Yen Kheng @LimYenKheng]

describing me a bit too accurately...

[Soares Chen @soareschen]

I am excited to share that after 3+ years of working as an independent researcher at my free time, my paper Ferrite: A Judgmental Embedding of Session Types in Rust has been accepted at ECOOP 2022! https://t.co/SqeN7Qsrs6

[Soares Chen @soareschen]

I am pleased to announce Mononym: a Rust library for creating unique type-level names for each value in Rust. Mononym enables a limited form of dependently-typed programming in Rust, and allows the Ghosts of Departed Proofs pattern to be used.

https://t.co/NVHEOapJfY

[Hui Xiang Chua @hxchuaruns]

Oh yay happy to see my blog on "Practising SQL without your own database" awarded Most Shared - Silver Badge and Most Shareable (Viral) Blogs! A great motivation for me to continue creating useful content :)

[johnwick @JQGoh]

Code Mechanic: Numpy Vectorization https://t.co/sqQP9NVKkM

[Hui Xiang Chua @hxchuaruns]

An attempt to improve chart published by @sporeMOH in their daily updates on vaccination figures.

PS: I only started collecting data from 1 Jul 2021.

Data can be found here: https://t.co/n8dq3NbPPL.

#dataviz #singapore #vaccination

[johnwick @JQGoh]

How Data Scientists Turned Against Statistics via @forbes https://t.co/2GCMYlwEK6

[johnwick @JQGoh]

https://t.co/n6Zt9QdjYx

[Data Science for Social Good @datascifellows]

Applications for the 2020 Data Science for Social Good Fellowship at Carnegie Mellon University are now open - Apply to be a fellow, mentor, project manager, or project partner

https://t.co/RseOJBzBAV

[Hui Xiang Chua @hxchuaruns]

Thank you for having me @pyconsg! :) #pyconsg #python #wids

[GlobalGiving @GlobalGiving]

Is AI is right for your nonprofit? Discover how to take advantage of powerful machine learning technology and learn about a new #AIForGood opportunity from @DataRobot. https://t.co/nQ1Cf6sPbU

[François Chollet @fchollet]

Sometimes -- often -- life deals you hands you simply can't play to success. It's not enough to do nothing wrong. That's why you should help those who aren't as lucky as you are, rather than always put the blame on them

[GlobalGiving @GlobalGiving]

We're delighted to partner with @DataRobot for their new #AIForGood program. Learn more about how they are helping nonprofits leverage data to accelerate community-led change.

[Neil deGrasse Tyson @neiltyson]

Dear @Disney, You got it right the first time. Water crystals have hexagonal “six-fold” symmetry.

You still have a few months to fix your #Frozen2 Movie Poster, unless the sequel takes place in another universe, where water crystalizes to different laws of physics.