A Go SDK for interacting with the Anthropic Claude API. This SDK allows you to send structured messages and handle responses from Anthropic's conversational AI models seamlessly within your Go applications.
Hello HackerNews Community,
Exciting news! I'm proud to introduce AIT-CodeX, our FREE AI-based coding tutor and consultant platform, an entry for the Google Prompt Hackathon S3: Coding Tutor and Consultant. Integrated with Google's PaLM2 AI, this platform aims to redefine how you learn and consult in coding.
Infrastructure:
Google Cloud CLI
Kubernetes Resource Model (KRM)
Terraform
Why Choose AIT-CodeX with Google PaLM2 AI?
Next-Level AI Guidance: Google PaLM2 AI offers intelligent mentorship across a diverse range of programming languages.
Extensive Language Support: Choose from 25+ programming languages—from C to YAML.
Cutting-Edge Infrastructure: Google Cloud CLI, Kubernetes, Terraform, and more!
Adaptable Learning Goals: Whether you're a beginner or a pro, our AI-driven courses are tailored to your needs.
Who Can Benefit?
No Experience
Beginners
Intermediate Coders
Advanced Experts
Unique Features of AIT-CodeX
Dynamic Learning: Execute commands to trigger real-time, AI-customized educational content.
Comprehensive Learning Path: Use the command sequence for a logical, step-by-step skill enhancement.
How to Get Started?
1⃣ Visit https://flowgpt.com/p/ait-codex and register for FREE.
2⃣ Kickstart your learning journey with commands like /languages, /infrastructure, #discover, #course, and more.
Hackathon Participation
We are excited to be a part of Google Prompt Hackathon S3. Your feedback could shape the future of AI in coding education!
No Downloads, Immediate Learning, Absolutely FREE!
Elevate Your Coding Skills Today at Zero Cost. Register Now
Feel free to share or tag someone who would benefit from this. Let's code the future, today!
Unlock your creativity and discover new possibilities with AIT-Visionaire, an AI assistant for idea generation.
I'm excited to introduce AIT-Visionaire, a free AI tool I just created to turbocharge your creativity and innovation. Simply describe your challenge or idea and Visionaire will use conceptual knowledge and imaginative thinking to spark new insights and possibilities.
Visionaire intelligently breaks down problems, reframes perspectives in novel ways, and generates "what if" scenarios to help you discover opportunities you may not have considered. It even assists in visualizing these ideas and formulating new strategies/prototypes, bringing a hit of inspiration to keep the ideas flowing
Attention all Gmail™ users! Say goodbye to tedious manual contact extraction and hello to a more efficient way of growing your contact list. Introducing AIT Contacts Extractor, the AI-powered add-on that streamlines the process of adding new contacts to your list.
With AIT Contacts Extractor, you can quickly and easily extract important information such as name, job title, organization, mailing address, phone number, and email address from any text within the message body. This information is then stored in separate columns in a Google Sheets™ spreadsheet, making it easy to keep track of your growing contact list.
Not only does AIT Contacts Extractor save you time and effort, it's also safe and secure. Rest assured that your email messages and contact information will not be collected, read, used, or transferred by the add-on.
So what are you waiting for? Take your contact list to the next level with AIT Contacts Extractor. Get it now and start building and growing your contact list without ever leaving your inbox.
After using the 5 free requests, you can continue using AIT Contacts Extractor by purchasing a subscription. To sweeten the deal, I have a special promo code for you that you can use to get a discount on your subscription. Simply send me a message to request the promo code, and I'll be happy to provide it to you. Keep streamlining your contact extraction process and take your contact list to the next level with AIT Contacts Extractor. Get your promo code now!
USPS address parsing is the process of breaking down a postal address into separate components such as recipient name, street address, city, state, and zip code. It is performed to standardize and validate the address information and improve the efficiency and accuracy of mail delivery. The process can be done manually or using automated software tools and algorithms and reference databases are used to identify and separate address components. USPS address parsing is important for organizations that need to manage and analyze postal address data.
Address parsing is difficult due to complex address formats, abbreviations and acronyms, non-standard addresses, variations in address components, inconsistent data quality, and international addresses. It requires sophisticated algorithms and reference databases to accurately identify and separate address components. Despite advancements in technology, address parsing remains a challenging task that requires high accuracy.
I selected 20,000 addresses from a database of 180 million records using algorithms such as Stratified Sampling, Cluster Sampling, Systematic Sampling, and Simple Random Sampling. I then fine-tuned the model of OpenAI for USPS address parsing. The fine-tuning process involved pre-processing the task-specific dataset, training the model on the data, evaluating its performance, and adjusting its hyper-parameters to achieve improved accuracy in parsing USPS addresses.
If you're writing code in multiple modules at the same time, you can use multi-module workspaces to easily build and run code in those modules.
In Go, a module is a collection of related Go source files located in a single directory. A workspace is a directory containing multiple modules. When you build a Go project, the build system reads the go.mod file in the project's root directory to determine which other modules the project depends on. The build system then downloads and installs any missing dependencies.
A) You can. The US government generates an itemized budget every year. It's several thousands of pages long, and usually the result of several large pieces of legislation. All of these are freely available online.
To a lesser extent, the states also generate itemized budgets. Most but not all of the state-level budget information is available online.
B) You can, to a lesser degree of detail, since non-profit tax returns are made available for public viewing by law. You can use Charity Navigator (fee required) or visit your nearest IRS office to view them. Many states also require non-profits above a certain size to make their financials available upon request.
You mean you can’t see where the money is going? If you know how much money you pay on different levels (local, national and so on) surely also those governments have itemized budgets? You can roughly say what amount you paid for different services then. Transfers between different levels of government could complicate it though.
I wish you weren't downvoted, this is exactly right.
The nice thing about S2 is that is subdivides cleanly: A square can be composed of smaller squares while hexagons can't be. This property makes S2 much more broadly useful for a bigger range of applications.
In H3, each hierarchical level of hexagon doesn't fit cleanly in the one below. For Uber's uses, this is acceptable because hexagons have more uniform adjacency but the "zoom in and out" math is pretty gnarly.
But even S2 had the funkyness of first mapping a sphere to a cube. They're both fairly interesting to read up on.
Could you give more details? After reading the article I think the main reason is the subdivision algorithm which for S2 is easier but for everything else hexagon seems like a better approach.
Subdividing tiles can be more important than everything else: knowing in which tile a point lies is a fundamental operation for a spatial indexing system and it has to be very easy and efficient.
It's also vital for correct aggregation: if you sum up some quantity over all the subdivisions of some area, you really want that sum to be equal to the quantity in that area. With this hexagonal pseudo-subdivision, it isn't.
I think as long as you're adding the leaves on the same resolution level it should be fine. the problem is when your storing objects in different levels as it would be possible on a KDB tree.
