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Google's AI Overviews are often so confidently wrong that I’ve lost all trust in them

Google's AI Overview combines Google Gemini's language models with Retrieval-Augmented Generation to generate summaries for search queries, but it can be unreliable due to issues in the retrieval and language generation process.
The AI can make erroneous leaps and draw strange conclusions, leading to famous gaffes like recommending glue on pizza or describing running with scissors as a cardio exercise.
Despite improvements made by Liz Reid, head of Google Search, users still trick the AI into fabricating information or hallucinating, raising concerns about its accuracy and trustworthiness.
Some queries deliberately mislead the AI, but genuine questions can also lead to unreliable results, especially since many users don't verify the sourced information.
Google's AI Overviews seem to avoid topics like finance, politics, health, and law, indicating limitations in handling more serious queries.
The article warns about blind trust in AI-generated content and suggests users critically evaluate and verify information rather than solely relying on AI summaries.
Regular use of technology like GPS can negatively impact cognitive skills, and reliance on AI for information without critical thinking can exacerbate the issue.
The author expresses skepticism about the reliabilities of AI tools like Google's AI Overviews and emphasizes the importance of seeking human-authored or verified articles for accurate information.
While AI tools may improve in the future, current reliability concerns persist, with high reported hallucination rates and increasing unreliability in AI-generated content according to recent reports.
The article underscores the need for users to be discerning and cautious when utilizing AI-generated summaries and to prioritize verified sources for crucial information.

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Dev

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The Importance of Real-World Experience for Engineering Students

Real-world experience plays a crucial role in shaping the mindset, communication, and decision-making abilities of engineering students.
Industrial training provides insights into time management, coordination, problem-solving in real-time, and the impact of small improvements in workflow.
Leadership roles in university help develop soft skills like confidence, responsibility, planning, leading, and adapting.
Volunteering enhances empathy, teamwork, and people skills, showing proactiveness, social responsibility, and preparing students for real challenges in their careers.

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5 Likes

Medium

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The Rise of Vibe Coding: How AI is Transforming Software Development

Vibe Coding is a new approach to software development where developers describe functionality in plain English and AI generates corresponding code.
This method democratizes programming, making it accessible to individuals without extensive coding backgrounds and enhancing efficiency.
Developers play a crucial role in guiding, testing, and refining AI-generated code, emphasizing the importance of human oversight.
Vibe Coding is gaining popularity due to its transformative potential in software development by combining human creativity with machine efficiency.

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Towards Data Science

199

How to Set the Number of Trees in Random Forest

Random Forest is a versatile machine learning tool widely used in various fields for making predictions and identifying important variables.
The optRF package helps determine the optimal number of decision trees needed to optimize Random Forest.
In R, the 'ranger' and 'optRF' packages can be used for Random Forest optimization and prediction.
The 'optRF' package provides functions like 'opt_prediction' for predicting responses and 'opt_importance' for variable selection.
By using the 'opt_prediction' function, the recommended number of trees is determined for making predictions.
The 'ranger' function with the optimal number of trees can be used to build a Random Forest model for making predictions.
To ascertain variable importance, 'ranger' function can be used with the 'importance' argument set as 'permutation'.
Increasing the number of trees in Random Forest can enhance the stability and reproducibility of the results.
Adding more trees helps in reducing randomness, but striking a balance is crucial to avoid unnecessary computation time.
The 'optRF' package analyzes the stability-number of trees relationship to determine the optimal number of trees efficiently.

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Towards Data Science

357

How to Build an AI Journal with LlamaIndex

This article discusses building an AI journal with LlamaIndex, focusing on seeking advice within the journal.
The implementation starts with passing all relevant content into the context, potentially leading to low precision and high costs.
Enhanced implementation involves Agentic RAG, combining dynamic decision-making and data retrieval for better precision in answers.
Creation and persistence of an index in a local directory with LlamaIndex SDK is straightforward for enhanced functionality.
Observations include the impact of parameters on LLM behavior and the importance of managing the inference capability based on the content.
To complete the seek-advice function, involving multiple Agents working together is recommended, leading to Agent Workflow implementation.
Agent Workflows can offer dynamic transitions based on LLM model function calls or explicit control over steps for a more personalized experience.
A custom workflow example illustrates a structured approach to agent interactions, controlling step transitions for effective advice generation.
The article emphasizes leveraging Agentic RAG and Customized Workflow with LlamaIndex to optimize user interactions in AI journal implementations.
The source code for this AI journal implementation can be found on GitHub for further exploration and development.

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20 Likes

Towards Data Science

322

The Automation Trap: Why Low-Code AI Models Fail When You Scale

Low-code AI platforms have simplified the process of building Machine Learning models, allowing anyone to create and deploy models without extensive coding knowledge.
While low-code platforms like Azure ML Designer and AWS SageMaker Canvas offer ease of use, they face challenges when scaled for high-traffic production.
Issues such as resource limitations, hidden state management, and limited monitoring capabilities hinder the scalability of low-code AI models.
The lack of control and scalability in low-code AI systems can lead to bottlenecks, unpredictability in state management, and difficulties in debugging.
Key considerations for making low-code models scalable include using auto-scaling services, isolating state management, monitoring production metrics, implementing load balancing, and continuous testing of models.
Low-code tools are beneficial for rapid prototyping, internal analytics, and simple software applications, but may not be suitable for high-traffic production environments.
When starting a low-code AI project, it's important to consider whether it's for prototyping or a scalable product, as low-code should be seen as an initial tool rather than a final solution.
Low-code AI platforms offer instant intelligence but may reveal faults like resource constraints, data leakage, and monitoring limitations as businesses grow.
Architectural issues in low-code AI models require more than just simple solutions and necessitate a thoughtful approach towards scalability and system design.
Considering scalability from the project's inception and implementing best practices can help mitigate the challenges associated with scaling low-code AI models.

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Towards Data Science

306

Agentic AI 102: Guardrails and Agent Evaluation

Guardrails are essential safety measures in AI to prevent harmful outputs, such as in ChatGPT or Gemini, which may restrict responses on sensitive topics like health or finance.
Guardrails AI provides predefined rules for implementing blocks in AI agents by installing and using specific modules through the command line.
Evaluation of AI agents is crucial, with tools like DeepEval offering methods such as G-Eval for assessing relevance, correctness, and clarity of responses from models like ChatGPT.
DeepEval's G-Eval method uses artificial intelligence to score the performance of chatbots or AI assistants, improving evaluation of generative AI systems.
Task completion evaluation, using DeepEval's TaskCompletionMetric, assesses an agent's ability to fulfill a given task, like summarizing topics from Wikipedia.
Agno's framework offers agent monitoring capabilities, tracking metrics like token usage and response time through its app for managing AI performance and costs.
By implementing Guardrails, evaluating AI agents, and monitoring their performance, developers can ensure responsible, accurate, and efficient AI behavior and outcomes.
Different evaluation methods like G-Eval and Task Completion Metric help in assessing the quality and performance of AI agents in various tasks and scenarios.
Model monitoring tools like those provided by Agno's framework enable developers to track and optimize AI agents' performance and resource usage effectively.
Ensuring ethical and safe AI behavior through guardrails, accurate evaluation methods, and effective monitoring tools is essential for building trustworthy and reliable AI agents.

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17 Likes

Medium

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Modeling Product Growth with Technical Debt: A Logistic Approach

The article introduces an extended logistic growth model to analyze the impact of technical debt and process fatigue on product growth.
The model includes a decay factor to simulate the slowdown in growth due to internal factors like technical debt, not just market saturation.
By tracking metrics like decay term, organizations can identify areas for improvement and maintain long-term growth.
The article compares two development styles - Fast & Rough vs. Slow & Stable - emphasizing the tradeoff between short-term speed and long-term sustainability.

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1 Like

Medium

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AI Won’t Kill Developers — But It Will Expose Those Who Don’t Think

AI won’t kill developers, but it will expose those who lack understanding and just copy-paste.
Coding is not just about typing but about thinking, and as long as AI can't think, the profession evolves.
Developers need to focus on asking the right questions, understanding the purpose of their code, and being architects rather than typists.
To stay relevant, developers should embrace AI, use it wisely, and remember they are accountable for the results even if AI assists in coding.

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Medium

425

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Mechanics Behind Async Iterators and For-Await-Of in JavaScript

JavaScript treats asynchronous values and iterable behavior as separate until used with for-await-of loop, where they come together.
An object is iterable when it has a method tied to Symbol.iterator that returns an object with a .next() method.
Async iterators are similar, but the .next() method returns a Promise, making the loop wait for Promise resolution.
for-await-of loop uses Symbol.asyncIterator to handle Promises and values with done flags.
Async generators combine yielding values like regular generators with waiting on Promises, allowing for pausing and waiting during execution.
The for-await-of loop waits for each yield from an async generator, allowing values to be processed as they arrive.
JavaScript engine turns async generators into paused state machines, enabling natural pause and resume functionality without manual handling.
Code within the for-await-of loop is paused using microtasks while waiting for Promises, ensuring asynchronous loops remain responsive.
for-await-of loop throws exceptions for rejected Promises, and the entire loop structure should be inside a try block to catch errors.
If a loop exits early, the engine checks for a .return() method in the iterator to clean up resources before finishing.

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25 Likes

Engadget

135

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Apple claims it's not blocking Epic from offering Fortnite in the EU

Apple claims it's not blocking Epic from offering Fortnite in the EU, according to a report from Bloomberg.
Apple says it did not take any action to remove the live version of Fortnite from alternative distribution marketplaces and wants Epic's European branch to resubmit the latest game update for publishing.
Epic CEO Tim Sweeney was hoping to relaunch Fortnite on the US App Store following a court victory, but Apple seems to be specific about not wanting Fortnite on the US App Store.
Fortnite was initially removed from the App Store due to Epic's attempt to skip Apple's in-app payment system, part of a larger plan to challenge the control of platforms like Apple and Google.

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8 Likes

Medium

354

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The Cost of Reflection on JVM Performance and Internal Access Checks

Calling a method reflectively in Java involves multiple internal steps on the JVM, including method lookup, creation of Method object, bytecode generation, and access checks.
Unlike direct method calls, reflective calls incur additional runtime work and checks, slowing down the process.
Direct method calls benefit from JIT compiler optimizations like inlining, which reflective calls lack due to runtime method determination.
Reflective calls involve argument conversion processes, boxing and unboxing of primitive types, leading to performance overhead.
Reflection in Java allows accessing private state through setAccessible(true), bypassing normal access controls, which was widely used in frameworks.
Java Platform Module System introduced boundaries for access control, requiring explicit declarations for reflective access, impacting frameworks relying on deep reflection.
Introduction of modules shifted the access control paradigm, making reflective access dependent on module-level permissions rather than setAccessible(true) alone.
Java deprecated the --illegal-access option, now enforcing access restrictions defined by module declarations, affecting reflection-heavy tools and frameworks.
Frameworks adapted by using new alternatives like sun.misc.Unsafe or VarHandle when faced with restricted reflective access due to module boundaries.
Reflection in Java continues to be used but with considerations for module-level boundaries and explicit permissions post-Java 9.
Understanding the complexities of reflective access in the context of Java modules helps navigate the limitations and requirements imposed by the JVM.

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21 Likes

Medium

135

RAG Powered AI App : How To Integrate REST API For REAL TIME Data for Knowledge Base (Line by Line…

The AI application demonstrates the interaction with a RESTful API server to obtain real-time data for use in Gemini Pro LLMs through Pinecone-powered RAG.
The tutorial covers the process flow from frontend to backend and showcases a RESTful server with over 4000 question-answer pairs from various categories.
Key aspects include sending GET requests to live API endpoints, parsing JSON for embeddings, managing large API responses with batch size, generating embeddings stored in Pinecone Vector DB, utilizing Gemini Pro LLM for response accuracy, and integrating knowledge from multiple sources.
The upcoming Part 21 will delve into integrating a time-series database, catering to developers, ML enthusiasts, researchers, and those interested in enhancing their applications with live web knowledge.

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8 Likes

Dev

433

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Max Mode in Cursor: Power, Access, and Missing Controls

Max Mode in Cursor allows users to access powerful models like GPT-4o and Claude Opus with expanded context and smarter completions, but lacks admin controls, prompting concerns over potential costs and unauthorized usage.
Any user can enable Max Mode without admin settings or permission gates, leading to immediate billing to the workspace without clear alerts or usage caps.
Max Mode operates on a usage-based pricing model, charging per token and billing usage directly to the shared workspace account, potentially causing surprise charges for enterprise teams.
Lack of admin controls in Max Mode poses challenges for enterprise accounts, including insufficient access control, budget safeguards, and compliance blind spots.
Suggestions include implementing role-based access controls, workspace-wide toggles, soft and hard usage caps, and project-specific allowances to mitigate risks associated with Max Mode.
In comparison to similar tools like GitHub Copilot and Cody, Cursor could enhance control features to align with enterprise needs, improving model access restrictions and usage analytics.
Workarounds for managing Max Mode usage currently include creating internal usage policies, assigning monitoring responsibilities, using billing data exports, and segmenting environments for testing purposes.
While Max Mode offers significant power to technical users, Cursor's rollout lacks essential admin toggles, usage caps, and real-time visibility, warranting caution in its adoption within larger teams.
Considering the potential risks and benefits of Max Mode, proactive management strategies such as monitoring usage, educating teams, and possibly restricting access through policies are recommended.
Further development of admin toggles, usage caps, and enhanced monitoring tools by Cursor could improve the suitability of Max Mode for broad implementation within enterprise environments.
To navigate the complexities of Max Mode usage effectively, staying informed about its implications, monitoring usage closely, and considering preventative actions can help organizations maximize its benefits while minimizing risks.

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26 Likes

Medium

166

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Leetcode #88 — Merge Sorted Array from Naive to Optimal Solution

Merge two sorted integer arrays nums1 and nums2 in-place.
Input: nums1, nums2, m, n where nums1 has m+n length and nums2 has n elements.
Constraints: nums1 and nums2 are already sorted in non-decreasing order.
Code implements merging nums2 into nums1 from the end by comparing elements.

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