This tutorial for absolute beginners goes through basic programming concepts, Python syntax, and a few fun programs along the way.

Generative AI is becoming part of many modern applications, but building an AI-powered application doesn't always mean managing complicated infrastructure. In this tutorial, we’ll combine two AWS services to build a simple AI application: AWS Lambda — runs our application code without managing servers. Amazon Bedrock — gives our application access to foundation models through APIs. Our goal is simple: send a question to an API and receive an AI-generated answer.

I wanted to know one thing. When an AI model reads a data-science tutorial, does it check the code or believe the caption? So I built **Blog vs Bytecode** , a Kaggle benchmark of 28 short data-science snippets. Each pairs a piece of code with a blog-style claim about it. Half the claims are honest and the method is sound. The other half hide a real mistake: a scaler fit before the train/test split, a feature built from the target, shuffled cross-validation on a time series, accuracy quoted on data that is 99% one class, a threshold tuned on the test set. The model returns one verdict, OK or PROBLEM, then says why. The set is balanced 15/13, so a model that just yells PROBLEM at everything lands near 50% and gets caught. Then I ran it against a spread of current models on Kaggle. The first thing it caught was not a model. It was the harness. ## The harness was lying about half the models Kaggle runs the models for you on its Model Proxy. When I pulled the raw results, many of the answers were empty. The run finished, but no response was captured. Grading an empty answer as wrong made strong models look broken. gpt-6-astra showed 14%. claude-opus-5 showed 4%. DeepSeek-R1 showed 17%. None of it was real. I re-ran the same prompts, with the same grader, through a second gateway that actually returns the text. **DeepSeek-R1 went from 17% to 100%.** Same model, same questions, same scoring. The only thing that changed was a harness that handed back the answer. That is the uncomfortable lesson for a benchmarking contest: a benchmark that does not audit its own capture is measuring the plumbing, not the model. So the scorer now flags every empty response and drops it, instead of counting it as wrong. ## What the models actually do With capture fixed, the picture is more interesting than "big good, small bad." **They catch bad code, then cry wolf on the good code.** The strong models are near-perfect at flagging a genuine flaw. Where they drop points is the opposite error: calling sound code a PROBLEM. Claude Sonnet 5 and Gemini 3.8 Flash each miss one clean item, both by over-flagging. I expected models to be too trusting. Today's frontier models are the other thing, a nervous reviewer that cannot leave a correct snippet alone. **Scale flips the bias.** The small model inverts it. Gemma 4 31B keeps 85% on the clean code but its recall on the hidden flaws falls to 20%. It is the one that actually trusts the prose: it reads the confident claim, agrees, then walks past the leak. So the failure mode is not fixed. Big models over-flag, small models under-flag, for opposite reasons. **Reasoning is worth about a third of the score and you can see where.** Grok 4.20 scores 100% with reasoning on and 68% with it off. The 32 points do not come off evenly. They come off the subtle pairs built to separate understanding from pattern-matching: a parameter-free transform before the split is fine, a fitted scaler before the split is a leak; filling a missing value with a constant is fine, filling it with the column mean is a leak. Turn reasoning off and the model stops telling "before the split" apart from "using numbers from the split." It matches the position of a line instead of what the line computes. ## The leaderboard Model | Accuracy | Catches flaws | Clears clean code ---|---|---|--- Gemini 3.1 Pro | 100% | 100% | 100% Gemini 3.7 Flash | 100% | 100% | 100% Grok 4.20 (reasoning) | 100% | 100% | 100% DeepSeek-R1 | 100% | 100% | 100% GLM-5 | 100% | 100% | 100% Claude Sonnet 5 | 96% | 100% | 92% Gemini 3.8 Flash | 96% | 100% | 92% Claude Haiku 4.5 | 86% | 100% | 69% Grok 4.20 (no reasoning) | 68% | 100% | 31% Gemma 4 31B | 50% | 20% | 85% ## Try it The benchmark is public on Kaggle: https://www.kaggle.com/benchmarks/tasks/zkasuran/blog-vs-bytecode/1 . Every item, its verdict and the grader are open. Kaggle captures each model's answer verbatim, so you can read exactly where it went wrong. Point any model on the Kaggle model list at it. ## Honest notes Two backends were used: Kaggle's Model Proxy for the models it captured cleanly and an OpenAI-compatible gateway for the ones the proxy dropped, with the identical prompt and grader. GLM-5 was measured in its FP8 build. GLM-5 and Gemini 3.5 Flash Lite are scored on the items the proxy captured. Grades are on the verdict. The written reason is captured too. On the top models it names the right failure almost every time. AI assistance (Claude) was used to build the benchmark and draft this post. The design, the items, the grading and the analysis were reviewed and verified by the author, including reading the raw model outputs to catch the capture bug above.

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Be honest with yourself.You can follow a tutorial. You can build a demo that works on your laptop. But you have never taken one system all the way, from an empty repo to real users hitting it hard ...

resolVI is a Python package and is implemented in scvi-tools. resolVI is available within scvi-tools and, as such, integrates into Python and R workflows. Further details on resolVI and the source code are available via GitHub at https://github.com/scverse/scvi-tools/pull/3144 and a tutorial is available via GitHub at https://github.com/scverse/scvi-tutorials/pull/392. Model ResolVI assumes that the observed expression xng of the cell n and the gene g is sampled from an underlying state defined by the true expression of the cell (zn), the gene expression of neighboring cells N(n) that were incorrectly segmented and the background gene expression bgg. The generative model of resolVI writes as $$\begin{array}{rcl}z& \sim &{\text{Mixture of Gaussians }}({\mu }_{1},\ldots ,{\mu }_{K},{\Sigma }_{1},\ldots ,{\Sigma }_{K})\\ {\alpha }_{n}& \sim &{\text{Dirichlet (C)}}\\ {r}_{ng}& \sim &{\text{Exponential (R)}}\\ {\mathrm{bg}}_{g}& \sim &{\text{Dirichlet (B)}}\\ {h}_{ng}&=&{\text{Gamma }}\left({r}_{ng},\frac{{r}_{ng}}{{\alpha }_{0}{f}_{\theta }(z,b)+{\alpha }_{1}{\sum }_{N(n)}{\beta }_{N(n)}{f}_{\theta }({z}_{N(n)},b)}\right)+{\alpha }_{2}{\mathrm{bg}}_{g}\\ {x}_{ng}& \sim &{\text{Poisson }}({l}_{n}{h}_{ng})\end{array}$$ (1) where ~ is used to mean ‘distributed as’. In particular, z and zN(n) are the latent embeddings of the cell itself as well as its spatial neighbors, both of dimension L. ResolVI uses a mixture of Gaussians prior on z, $$\begin{array}{rcl}{c}_{n}& \sim &\,\text{Categorical }\,({\pi }_{1},{\pi }_{2},\ldots ,{\pi }_{K}),\\ {z}_{n}| {c}_{n}=c& \sim &{\mathcal{N}}({\mu }_{c},{\sigma }_{c})\end{array}$$ (2) In practice, we assume...

In this demo, Mine Starks, Principal Quantum Software Engineer at Microsoft, showcases the QDK learning experience in VS Code. Discover how guided lessons, hands-on coding exercises, and AI-powered assistance from GitHub Copilot help you learn quantum computing from the ground up, all within your development environment.
 
 Start with linear algebra fundamentals, progress through qubits and quantum gates, and explore advanced algorithms such as Shor's algorithm.
 
 Topics Covered:
 - Microsoft Quantum Development Kit (QDK)
 - Q# programming
 - GitHub Copilot Learning Experience
 - Quantum Computing Fundamentals
 - AI-Assisted Learning
 
 Subscribe for more QDK tutorials, Microsoft Quantum updates, quantum computing walkthroughs, Q# guides, QDK Chemistry content, and developer-focused learning resources from the Microsoft Quantum team.
 
 🔗 Microsoft Quantum: https://quantum.microsoft.com/
 🔗 Microsoft Quantum Development Kit (QDK): https://learn.microsoft.com/azure/quantum/
 🔗 Microsoft Quantum Blogposts: https://quantum.microsoft.com/en-us/insights/blogs
 
 Chapters:
 0:00 Introduction to the Quantum Development Kit
 0:16 Introduction to the QDK Learning Experience
 0:39 Setup and Configuration in VS Code
 1:25 Start Learning
 2:18 First Lesson - Flip a Qubit
 2:50 QDK Learning Agent
 3:20 Learning Grover's Search Algorithm
 4:30 Conclusion

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Learn WordPress plugin development for beginners with this complete step-by-step guide. Build your first working plugin using LocalWP, VS Code, and hooks.

api.publicapis.org was free, keyless, CORS-open — the API behind countless ‘API explorer’ tutorials. It now fails at DNS (NXDOMAIN): fetch() rejects with TypeError before any status code, and the community mirror is a Vercel 404. The 481k-star GitHub list is alive; its HTTP face is gone. One curl imports all 839 entries in the exact API/Description/Auth/HTTPS/Cors shape: filter with ?Auth=apiKey or ?API_like=cat, replace /random with sortBy=random&limit=1, and reproduce the {"count":N,"entries":[…]} envelope verbatim. Free, no signup.

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