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Return of the king: Open AI’s Christmas Nightmare

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Prelude

Plastic influencers

Plastic influencer. AI Fanboy. Cardboard expert.

All terms entering the modern lexicon to describe the wave of ‘hype’ surrounding AI. I’ve long been a skeptic of some of the more outlandish and grandiose claims in the GenAI scene.

  • 1/ Programmers will disappear
  • 2/ AGI will arrive in 2024
  • 3/ All jobs will be automated
  • 4/ Robots will become conscious (Skynet)

All this baseless hyperbole without even delving into the more extremist views (there is a Reddit forum called singularity that has 3.4 million members).

I’m particularly bemused by the projection of emotion and fantasy onto computer algorithms capable of doing cool stuff. I believe that many brilliant people who subscribe to this Skynet perception of AI consciousness are at risk of losing their sanity. I’m not one of them.

My recent blogs have been in contradiction to the mainstream and somewhat fantastical AI world view 👇


AI powered voice chat: Lipstick on a pig (June 2024)

All these APIs are doing is converting audio to text, processing it through a language model, and then converting it back to audio. It might seem sophisticated on the surface but underneath it’s just basic text generation in a robot’s voice. Each individual system is comprehensive and reasonably mature, but glue them all together on our proverbial pig and there is no real understanding of the nuances of audio interactions. If it looks like a pig, squeals like a pig and walks like a pig. It’s a pig. Even if it’s wearing lipstick.


Generative AI: Cracking nuts with a sledgehammer (July 2024)

The barrier for excellence has never been so low, because the competition is increasingly with an algorithm and its unengaged and inexpert master. The robot will never reach true expertise, because there will never be a sufficient dataset of genuine experts to crowdsource from. And crowdsourcing takes the average result, not the best one. The robot doesn’t think. It repeats.


The lie of Agentic Frameworks (Dec 2024)

The problem with providing a tool or framework that allows you to abstract functionality is that it comes with a set of assumptions. When I buy a hammer, I assume it will work. When I buy a pressure cleaner, I assume it will work. The problem is that when I use a framework, I assume it will work. But this is quite literally impossible given the maturity of the underlying technology. Far from increasing adoption, Agentic Frameworks are selling an illusion on top of highly controlled demos and finite use cases that will never actually work in the hands of the typical user (and there are millions…).

This preface is to make a point.

Believe me when I say that I don’t say this lightly.

In terms of building practical applications with GenAI, what Google has just done with Gemini 2.0 flash has changed absolutely everything. Everything.

And no one saw it coming.

A Christmas Nativity

How Open AI’s theatre became a pantomine

One of my parents favourite stories is how when I was 5 years old, I was given a part in the local nativity play. Cast as a tree, my role was to silently adorn the set while the older and more capable children performed their interpretation of the birth of Jesus Christ.

I wasn’t particularly happy with this minor role.

Over the next 10-15 minutes, I followed the cast about stage, stealing their lines and thundering out my own entirely different interpretation of the play.

Interjecting at perfect moments, performing at others. It was a masterclass of disruption, and every giggle and teary eye from the watching crowd goaded me into more. It was ruthless destruction.

The performance descended into farce, the audience crying with laughter; the actors bemused and confused.

The laughter encouraged me, it became a crescendo.

The play was converted into pantomime, the job complete. To this day it remains a tale told at dinner parties to new and younger family members.

Of course, the play unfolding in Christmas 2024 is Open AI’s 12 days of Christmas and how Google has not just stolen their thunder, but commandeered the narrative, stolen the limelight and turned a Christmas celebration from OpenAI into a winter nightmare.

I, (like most rational people), tuned into the 12 days of Christmas by OpenAI with a healthy degree of skepticism, and watched as they showed demos of phone calls and astronomically expensive & slow API calls to a marginally improved LLM model, and felt reassured that my cynical world view was validated.

Then something happened.

It happened with perfect timing; theatre at it’s best.

Like an earthquake the repercussions are coming and they will be felt by everyone and seen in every AI product in the near future.

I thought Google had dropped the ball on AI, we all did. They were just irrelevant in all practical usages. Quality was poor, functionality was limited.

It turns out that they didn’t drop the ball and they weren’t asleep on the job. They were simply leaving the competition (now children by comparison) to wrestle with Beta releases, barely functioning APIs and scale issues while quietly building the tooling that is necessary to effectively use GenAI in production.

They timed their entrance to maximum effect.

Until a week ago I didn’t even have a live Google API Key.

This week, I’m in the process of migrating every single one of my services.

This may seem rash, but let me explain.

Scientists vs Builders

The difference between theory and practice

There are two different factions within the world of AI right now; scientists and builders.

The pioneers and scientists are seeking AGI and novel use cases; this includes important work such as new approaches to cancer treatments or looking for academic breakthroughs in Quantum physics. This can be theoretical or even in some cases some green shoots of practical use cases, especially in the domain of robotics for example.

These folk are interested in pursuing AGI and adapting GenAI to a more hybrid form of intelligence that will exponentially increase utility over current LLMs. This may take years, it may take generations (probably!).

I’m firmly and unashamedly in the second faction; we are builders.

GenAI is already capable of incredible stuff. Things that a year or two ago would have been impossible. I want to build stuff that works, right now.

The job at hand is working with available LLMs and APIs and seeing what use cases we can implement.

A builder needs tools and my stack was derived from countless hours spent testing the utility of all the available APIs and models.

  • 1/ Claude 3.5 Sonnet for Coding (Code)
  • 2/ OpenAI APIs for structured data w/ reasoning (Orchestration)
  • 3/ Groq / Fireworks AI APIs for cheap and instant inference (Fast inference)
  • 4/ Llama for local/on device (Edge computing)

I thought this stack would be solid for the next 3-5 years.

To be honest, I wasn’t really interested in any GenAI model that wasn’t listed above, I wasn’t even paying attention to the Gemini Flash v2.0.

I’m paying attention now.

How Agents work

2025, the year of the Agent.

We all know that 2025 is the year of the Agents, social media won’t stop telling us.

I hate hype trains but the underlying truth is that AI systems are now basically capable of ‘semi-reliably’ taking actions on our behalf. Thus, it is fair to say that there will be loads of popular software released in 2025 that will use this paradigm.

A typical agentic flow goes something like this.

We receive an instruction (Book a flight, call my mum, make my breakfast) which is interpreted by a Prompt. A prompt is usually executed via API, hence your OpenAI or Groq or Fireworks AI API). That prompt calls a tool (Skyscanner, Web search) which gets the result and calls some code setup by the developer and does “stuff”.

The result of this “stuff” is then returned to another Prompt and the cycle continues (nJumps) until we have performed the action. Hurrah.

It doesn’t look like the cleanest architecture does it?

If any of these API calls fails or returns an unexpected result, the whole chain is broken. Dozens of Python Frameworks have emerged to abstract this problem, but they can’t solve it. Tooling is improving, we can now see errors in execution, validate structured data and build chains with something approaching reliability, hence the hype for Agent 2025.

But the above architecture remains convoluted, complex and unreliable. Despite this, it is also the only way we had to unlock the potential of GenAI in Agentic flows.

In Dec 2024 Google has just made the above agentic model obsolete before it has even become ubiquitous.

The primary reasons are as follows:

  • 1/ Native search
  • 2/ Integrated orchestration
  • 3/ Multi-modal (which works!)

Google vs OpenAI & Perplexity

Native tooling: Search that works

Have a read of the Gemini API docs, and bear in mind that this isn’t a proposal or a fantasy, but an API that works and can provide results in milliseconds.

Google’s integrated search is reliable and also works quickly. Rivals such as Perplexity have a text based AI search engine, it has its place in the wider landscape but bear in mind that this the value proposition of an AI Unicorn has now been integrated as a ‘feature’ of Gemini Flash v2.0.

Perplexity AI’s purpose and reason for existence has been assumed within an actual AI model that is capable of the same quality and speed of result with massive utility in other areas as well.

The fact that Google owns a proprietary Search engine is critical here. They have a genuinely “Native Tool” in every sense, bundled into the same API serving the inference model that can search the internet available by just adding some text to the API call.

Ah, but OpenAI can do that too I hear you say?

OpenAI can’t compete. Their search is not native (or not mature) and that is important. It really shows. They have a “Realtime API”, but it doesn’t work that well and is noticeably slower and buggier than Google’s Gemini Flash v2.0 implementation. In real time more than any other domain, latency is everything. The results are not even close.

OpenAI interaction Example

A real exchange with OpenAI realtime

,

Google literally runs the search request WHILE the model is responding and has the infrastructure to provide the answer before you have read the response. This small detail covers the critical milliseconds that change the interaction experience from “Lipstick on a Pig” to the “real f**king deal”.

Google’s integrated search works, and it works really really quickly.

Loads of talk in the AI world about how no-one has a moat.

Well Google just filled up a giant moat with Christmas Joy and pulled the drawbridge.

Price, Speed, Quality… Choose two? Hmmmm…

Google is winning on three counts.

Merry Christmas OpenAI.

Google vs Python Frameworks

Simple Agentic flows: RIP Python abstractions.

But it doesn’t stop there. Google has changed the game in terms of Agentic flows. Search the internet for “AI Tools” and you will find mountains of frameworks, code repos and projects that are basically doing the same thing.

  • 1/ Search Internet; Check
  • 2/ Scape website; Check
  • 3/ Convert to markdown; Check
  • 4/ Run code; Check

All these tools are automating search, retrieval and code execution. Have a look at the Langchain Tools for example.

The thing is, Google has just integrated this into their API, a single endpoint to handle all of the above. It is now essentially a solved problem. We no longer need complex agentic flows for many many use cases.

The below diagram from OpenAI shows how function calling works for Agents.

Tools as described by OpenAI

Until now, we have been relying on managing the execution environment outside of the API calls.

Google has just built most of that functionality into a core API that can be used by developers.

For example, if I want to use Llama 3.3 to search the internet, I can do tool calling as follows.

Llama3 tool use

This same flow with Gemini Flash v2.0:

Gemini v2 tool use

Back to the previous point, Speed, Quality, Cost…

Google just chose all 3.

Nearly all agents are variations of search, retrieval (convert to markdown and inject into prompt) and arbitrary code execution with a sprinkling of private data. Except for the data (almost definitely coming soon…), these are now core concerns, which has made a lot of Agentic systems obsolete before they have been launched.

It won’t be long before we also have native plugins to your Google data sources (a logical next step), at which point except for a rare few scaled and highly complex AI systems, basically all the current frameworks and processes are just convoluted implementations of what can be achieved better, faster and cheaper in a single API call.

The relevance of this from an architectural point of view, is that instead of building chained and complex flows, I can refine a single simple model. Everything just became a lot simpler

Even if we can’t do everything we need right now, the line in the sand has been drawn and most common “tools” must become core concerns, integrated into APIs by providers. We don’t need to DIY our own Agents anymore, we have reliable, scaled and fast APIs to work with.

Bye bye Python frameworks. (don’t stay in touch).

Multi-Modal that works

Magical human to machine UX

Like me, you are probably a bit burned by all the multi-modal ‘demo’ examples of Audio/Video usage. I remember being so excited to try audio-streaming (I’ve been developing on WebRTC for years and in a past life founded an eCommerce video streaming tool).

The potential is obvious, but the whole thing just doesn’t feel right. For an example go to the OpenAI playground and try out their realtime API. It shows potential, but is miles away from being an enjoyable user experience. Most users just want an experience that “works”. Those milliseconds and natural intonations are not details, they are the very essence of the product.

Gemini Flash v2.0 is the first model that gave me the “wow” moment that I had when I first started using Claude for coding. It is the same feeling as the first time you sceptically asked ChatGPT a question and the “machine” gave you a human response.

The latency, the pauses, the voice intonation. Google has NAILED it. It is still obviously an AI system, but that was never the problem. The problem was always the pauses, the interruptions, the way that the model interacted with humans.

I don’t mind talking to a machine, assuming the machine is knowledgeable, able to interact and capable of doing the things that I need it to do. This is 100% the first time I’ve actually seen a model capable of providing this experience, and the ramifications are tremendous.

If you were excited by audio or video interactions and a bit sceptical of the models. Go give Gemini Flash v2.0 a try. Google has obviously invested time, effort and resources into solving issues about latency and cost. No other AI model that I have tried even comes close.

Conclusion

There was a dream that was the UX of Generative AI.

I’m as excited as the first time that I asked ChatGPT to write a linkedin post all those years ago. At this stage of my life and involvement with GenAI, that isn’t particularly easy.

I didn’t expect this moment to come so soon.

We now have a reality with a cheap, fast and highly capable model that we can interact with in real time.

This is literally the first time in my life that I can speak to a computer, and feel like it understands me, can respond to me, and take actions on my behalf. It isn’t a complex agent, it is a single API call.

This is a technical achievement that will reverberate through the AI world, even if many haven’t yet realised.

Apart from the natural interface and interactions, the model is capable of natively searching the internet, executing code and giving me the response in the time it takes to form a sentence.

There was a dream that was the UX of Generative AI.

In December 2024 it became a reality.

And it’s cheap…

And it’s scalable…

Now if you will excuse me, I’m off to build stuff.

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Géminis, mayo de 2025: Su horóscopo mensual

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Para Gemini carismático, adaptable y curioso: esto es lo que puede esperar disfrutar, trabajar y recibir durante todo el mes de mayo.

Nuestras mentes subconscientes son más perceptivas a los cambios inminentes de lo que nuestras mentes conscientes podrían darse cuenta. Al igual que los temblores antes de un tsunami, las partes más profundas de nuestros corazones y mentes a menudo pueden sentir cuando está a punto de tener lugar un cambio significativo. Ese ciertamente parece ser el caso para usted este mes, Géminis, ya que su pronóstico comienza con un cuadrado desafiante entre la luna creciente de la depilación y su planeta gobernante, Mercurio. Iniciar un plan de acción preciso puede ser más difícil. La niebla cerebral y la falta general de motivación son igualmente probables culpables. Tome nota de lo que le ha estado molestando y mantenga esos registros en un lugar donde pueda acceder fácilmente a ellos. Incluso las molestias o ansiedades aparentemente menores pueden ser guías útiles al navegar por el cambio celestial principal de este mes.

Esa transición tiene lugar el 4 de mayo, cuando Plutón se retrógrado, un largo período celestial que afectará los pronósticos cósmicos en los próximos meses. A pesar de la inmensa distancia de este planeta enano desde nuestro punto de vista terrenal, la influencia de Plutón sobre nuestras mentes subconscientes, la transformación social, los tabúes, la muerte y el renacimiento lo convierten en un retrógrado notable. Si otros períodos retrógrados molestos como los de Mercurio son los sutiles susurros de los vientos que atraviesan las grietas en una pared, Plutón retrógrado es el tornado que derriba toda la estructura. Las transformaciones de Plutón son vastas y duraderas. Se pertenecen a aspectos de la existencia que trascienden nuestras vidas individuales mientras afectan cada parte de ellos.

Varios días después, el 7 de mayo, Mercurio forma una potente conjunción con Quirón en Aries. Quirón es un planeta enano que gobierna nuestras vulnerabilidades y heridas emocionales. Influye en la forma en que transformamos nuestro dolor en algo más útil y positivo, ya sea que sea sabiduría que podamos usar o el conocimiento que podemos compartir con los demás. La destreza comunicativa de Mercurio y el intelecto agudo se prestan a una mejor comprensión y, a su vez, el procesamiento de duelos pasados. Nunca es demasiado tarde para aprender de un viejo error, Géminis. Hacerlo puede ser la diferencia entre que esa herida emocional sea una costra dolorida y una cicatriz sutil. No puedes cambiar lo que ya ha pasado. Pero puedes cambiar a donde vayas a continuación.

Su planeta gobernante pasa a Tauro gobernado por la Tierra el mismo día que forma una oposición directa a la luna gibrosa. El mercurio en Tauro promueve la firmeza, la confianza y la estabilidad. También puede conducir a la terquedad, la ingenuidad y la alienación. Tenga cuidado de cómo ejerce esta energía cósmica, Stargazer. El enfrentamiento celestial de Mercurio con la luna gibosa de depilación crea conflicto entre la persona en la que se encuentra en este mismo momento y la persona que tiene el potencial de ser. La luna gibosa de depilación lo llama para evaluar su progreso hasta ahora. Si tuviera que mantener este mismo camino, ¿dónde estaría bajo el brillo de la luna llena en unos días? Si no estás contento con la respuesta, ahora es el momento de redirigir.

Tendrá la oportunidad de calificar sus respuestas, por así decirlo, cuando la luna llena alcanza su máxima fuerza en Scorpio el 12 de mayo. Una luna llena en Scorpio puede sonar intimidante (lo siento, Scorpios, pero su reputación le precede). Sin embargo, no seas tan rápido para asumir lo peor. Scorpio es un dominio celestial que bloquea el enfoque en la dinámica de poder, la mente subconsciente y los temas tabú u opaco como la sexualidad, la identidad, el propósito de la vida, la fe y lo que significa ser exitoso y contenido. Bajo el resplandor revelador de la luna llena, el Cosmos lo dirigirá hacia el tema que más ha estado sopesando mucho en su mente. El flujo de energía estará abierto durante este tiempo, Géminis. Capitalizar la oportunidad de perfeccionar su fuerza.

Un cambio tangible hacia el descanso y la recalibración comienza el 16 de mayo. En este día, la luna gibrosa disminuyendo forma un trígono armonioso con mercurio. La disminución de la luna gibosa nos empuja a liberar viejos comportamientos, ideas o incluso relaciones que ya no nos sirven como antes. Dos días después, Mercurio y Marte forman una plaza desafiante. Esta alineación envía un mensaje claro: ahora no es el momento de actuar. Habrá muchas posibilidades de afirmarse en el futuro. En este momento, las estrellas te instan a que atiendan tus propias necesidades y deseos.

El sol ingresa a su dominio celestial, iniciando la temporada de Géminis, el 20 de mayo. Además de fortalecer su sentido general de sí mismo y propósito, la ubicación del sol promueve el pensamiento flexible y una identidad maleable. Para ser claros, esto no es lo mismo que perderse por completo, Stargazer. Es simplemente una oportunidad para explorar otras partes de ti mismo que podría haber pensado que no existía. Llevas multitudes. Incluso en los últimos días de su vida, aún habrá profundidades inexploradas. Eso es lo que hace que esta información sea tan satisfactoria y la vida tan gratificante. Descubrir nuevas facetas de su identidad no es un castigo, a pesar de la mayor carga de trabajo emocional y mental. La oportunidad de mirar a tu sí mismo siempre es una bendición.

Las estrellas continúan priorizando el cambio y la innovación a medida que Mercurio y Urano se unen bajo Tauro. Urano podría tener una mala reputación por ser caótico y rebelde. Pero con Mercurio en la mezcla, esta alineación parece ser más audaz e innovadora que destructiva. Explore las posibilidades ante usted y absorbe lo que pueda. La luna nueva en su dominio celestial el 27 de mayo (que también se reúne con su planeta gobernante) ofrece el momento perfecto para reflexionar sobre el Intel que reunió. ¿Cómo se comparan las viejas y nuevas versiones de ti mismo? ¿Contraste? Equilibrio entre los dos mentiras en las respuestas a cualquier pregunta.

May será un momento especialmente tumultuoso en el cosmos, pero al menos terminaste en una buena base. El 27 de mayo también marca el comienzo de un trígono entre Plutón y Mercurio, que es seguido de cerca por la conjunción del Sol con su planeta gobernante el 30 de mayo. Se está produciendo un cambio importante, y todos los signos cósmicos apuntan a que sea para mejor. Abraza las mariposas en tu estómago, Géminis. Grandes cosas están en camino.

Así concluye sus aspectos más destacados mensuales. Para análisis celestiales más específicos, asegúrese de leer su horóscopo diario y semanal también. ¡Buena suerte, Géminis! Nos vemos el próximo mes.

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How Would I Learn to Code with ChatGPT if I Had to Start Again

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Coding has been a part of my life since I was 10. From modifying HTML & CSS for my Friendster profile during the simple internet days to exploring SQL injections for the thrill, building a three-legged robot for fun, and lately diving into Python coding, my coding journey has been diverse and fun!

Here’s what I’ve learned from various programming approaches.

The way I learn coding is always similar; As people say, mostly it’s just copy-pasting. 😅

When it comes to building something in the coding world, here’s a breakdown of my method:

  1. Choose the Right Framework or Library
  2. Learn from Past Projects
  3. Break It Down into Steps
    Slice your project into actionable item steps, making development less overwhelming.
  4. Google Each Chunk
    For every step, consult Google/Bing/DuckDuckGo/any search engine you prefer for insights, guidance, and potential solutions.
  5. Start Coding
    Try to implement each step systematically.

However, even the most well-thought-out code can encounter bugs. Here’s my strategy for troubleshooting:

1. Check Framework Documentation: ALWAYS read the docs!

2. Google and Stack Overflow Search: search on Google and Stack Overflow. Example keyword would be:

site:stackoverflow.com [coding language] [library] error [error message]

site:stackoverflow.com python error ImportError: pandas module not found

– Stack Overflow Solutions: If the issue is already on Stack Overflow, I look for the most upvoted comments and solutions, often finding a quick and reliable answer.
– Trust My Intuition: When Stack Overflow doesn’t have the answer, I trust my intuition to search for trustworthy sources on Google; GeeksForGeeks, Kaggle, W3School, and Towards Data Science for DS stuff 😉

3. Copy-Paste the Code Solution

4. Verify and Test: The final step includes checking the modified code thoroughly and testing it to ensure it runs as intended.

And Voila you just solve the bug!

Photo by Stephen Hocking on Unsplash

Isn’t it beautiful?

But in reality, are we still doing this?!

Lately, I’ve noticed a shift in how new coders are tackling coding. I’ve been teaching how to code professionally for about three years now, bouncing around in coding boot camps and guest lecturing at universities and corporate training. The way coders are getting into code learning has changed a bit.

I usually tell the fresh faces to stick with the old-school method of browsing and googling for answers, but people are still using ChatGPT eventually. And their alibi is

“Having ChatGPT (for coding) is like having an extra study buddy -who chats with you like a regular person”.

It comes in handy, especially when you’re still trying to wrap your head around things from search results and documentation — to develop what is so-called programmer intuition.

Now, don’t get me wrong, I’m all for the basics. Browsing, reading docs, and throwing questions into the community pot — those are solid moves, in my book. Relying solely on ChatGPT might be a bit much. Sure, it can whip up a speedy summary of answers, but the traditional browsing methods give you the freedom to pick and choose, to experiment a bit, which is pretty crucial in the coding world.

But, I’ve gotta give credit where it’s due — ChatGPT is lightning-fast at giving out answers, especially when you’re still trying to figure out the right from the wrong in search results and docs.

I realize this shift of using ChatGPT as a study buddy is not only happening in the coding scene, Chatgpt has revolutionized the way people learn, I even use ChatGPT to fix my grammar for this post, sorry Grammarly.

Saying no to ChatGPT is like saying no to search engines in the early 2000 era. While ChatGPT may come with biases and hallucinations, similar to search engines having unreliable information or hoaxes. When ChatGPT is used appropriately, it can expedite the learning process.

Now, let’s imagine a real-life scenario where ChatGPT could help you by being your coding buddy to help with debugging.

Scenario: Debugging a Python Script

Imagine you’re working on a Python script for a project, and you encounter an unexpected error that you can’t solve.

Here is how I used to be taught to do it — the era before ChatGPT.

Browsing Approach:

  1. Check the Documentation:

Start by checking the Python documentation for the module or function causing the error.

For example:
– visit https://scikit-learn.org/stable/modules/ for Scikit Learn Doc

2. Search on Google & Stack Overflow:

If the documentation doesn’t provide a solution, you turn to Google and Stack Overflow. Scan through various forum threads and discussions to find a similar issue and its resolution.

StackOverflow Thread

3. Trust Your Intuition:

If the issue is unique or not well-documented, trust your intuition! You might explore articles and sources on Google that you’ve found trustworthy in the past, and try to adapt similar solutions to your problem.

Google Search Result

You can see that on the search result above, the results are from W3school – (trusted coding tutorial site, great for cheatsheet) and the other 2 results are official Pandas documentation. You can see that search engines do suggest users look at the official documentation. 😉

And this is how you can use ChatGPT to help you debug an issue.

New Approach with ChatGPT:

  1. Engage ChatGPT in Conversations:

Instead of only navigating through documentation and forums, you can engage ChatGPT in a conversation. Provide a concise description of the error and ask. For example,

“I’m encountering an issue in my [programming language] script where [describe the error]. Can you help me understand what might be causing this and suggest a possible solution?”

Engage ChatGPT in Conversations

2. Clarify Concepts with ChatGPT:

If the error is related to a concept you are struggling to grasp, you can ask ChatGPT to explain that concept. For example,

“Explain how [specific concept] works in [programming language]? I think it might be related to the error I’m facing. The error is: [the error]”

Clarify Concepts with ChatGPT

3. Seek Recommendations for Troubleshooting:

You ask ChatGPT for general tips on troubleshooting Python scripts. For instance,

“What are some common strategies for dealing with [issue]? Any recommendations on tools or techniques?”

Using ChatGPT as coding buddy

Potential Advantages:

  • Personalized Guidance: ChatGPT can provide personalized guidance based on the specific details you provide about the error and your understanding of the problem.
  • Concept Clarification: You can seek explanations and clarifications on concepts directly from ChatGPT leveraging their LLM capability.
  • Efficient Troubleshooting: ChatGPT might offer concise and relevant tips for troubleshooting, potentially streamlining the debugging process.

Possible Limitations:

Now let’s talk about the cons of relying on ChatGPT 100%. I saw these issues a lot in my student’s journey on using ChatGPT. Post ChatGPT era, my students just copied and pasted the 1-line error message from their Command Line Interface despite the error being 100 lines and linked to some modules and dependencies. Asking ChatGPT to explain the workaround by providing a 1 line error code might work sometimes, or worse — it might add 1–2 hour manhour of debugging.

ChatGPT comes with a limitation of not being able to see the context of your code. For sure, you can always give a context of your code. On a more complex code, you might not be able to give every line of code to ChatGPT. The fact that Chat GPT only sees the small portion of your code, ChatGPT will either assume the rest of the code based on its knowledge base or hallucinate.

These are the possible limitations of using ChatGPT:

  • Lack of Real-Time Dynamic Interaction: While ChatGPT provides valuable insights, it lacks the real-time interaction and dynamic back-and-forth that forums or discussion threads might offer. On StackOverflow, you might have 10 different people who would suggest 3 different solutions which you can compare either by DIY ( do it yourself, try it out) or see the number of upvotes.
  • Dependence on Past Knowledge: The quality of ChatGPT’s response depends on the information it has been trained on, and it may not be aware of the latest framework updates or specific details of your project.
  • Might add extra Debugging Time: ChatGPT does not have a context of your full code, so it might lead you to more debugging time.
  • Limited Understanding of Concept: The traditional browsing methods give you the freedom to pick and choose, to experiment a bit, which is pretty crucial in the coding world. If you know how to handpick the right source, you probably learn more from browsing on your own than relying on the ChatGPT general model.
    Unless you ask a language model that is trained and specialized in coding and tech concepts, research papers on coding materials, or famous deep learning lectures from Andrew Ng, Yann Le Cunn’s tweet on X (formerly Twitter), pretty much ChatGPT would just give a general answer.

This scenario showcases how ChatGPT can be a valuable tool in your coding toolkit, especially for obtaining personalized guidance and clarifying concepts. Remember to balance ChatGPT’s assistance with the methods of browsing and ask the community, keeping in mind its strengths and limitations.


Final Thoughts

Things I would recommend for a coder

If you really want to leverage the autocompletion model; instead of solely using ChatGPT, try using VScode extensions for auto code-completion tasks such as CodeGPT — GPT4 extension on VScode, GitHub Copilot, or Google Colab Autocomplete AI tools in Google Colab.

Auto code completion on Google Colab

As you can see in the screenshot above, Google Colab automatically gives the user suggestions on what code comes next.

Another alternative is Github Copilot. With GitHub Copilot, you can get an AI-based suggestion in real-time. GitHub Copilot suggests code completions as developers type and turn prompts into coding suggestions based on the project’s context and style conventions. As per this release from Github, Copilot Chat is now powered by OpenAI GPT-4 (a similiar model that ChatGPT is using).

Github Copilot Example — image by Github

I have been actively using CodeGPT as a VSCode Extension before I knew that Github Copilot is accessible for free if you are in education program. CodeGPT Co has 1M download to this date on the VSCode Extension Marketplace. CodeGPT allows seamless integration with the ChatGPT API, Google PaLM 2, and Meta Llama.
You can get code suggestions through comments, here is how:

  • Write a comment asking for a specific code
  • Press cmd + shift + i
  • Use the code 😎

You can also initiate a chat via the extension in the menu and jump into coding conversations 💬

As I reflect on my coding journey, the invaluable lesson learned is that there’s no one-size-fits-all approach to learning. It’s essential to embrace a diverse array of learning methods, seamlessly blending traditional practices like browsing and community interaction with the innovative capabilities of tools like ChatGPT and auto code-completion tools.

What to Do:

  • Utilize Tailored Learning Resources: Make the most of ChatGPT’s recommendations for learning materials.
  • Collaborate for Problem-Solving: Utilize ChatGPT as a collaborative partner as if you are coding with your friends.

What Not to Do:

  • Over-Dependence on ChatGPT: Avoid relying solely on ChatGPT and ensure a balanced approach to foster independent problem-solving skills.
  • Neglect Real-Time Interaction with Coding Community: While ChatGPT offers valuable insights, don’t neglect the benefits of real-time interaction and feedback from coding communities. That also helps build a reputation in the community
  • Disregard Practical Coding Practice: Balance ChatGPT guidance with hands-on coding practice to reinforce theoretical knowledge with practical application.

Let me know in the comments how you use ChatGPT to help you code!
Happy coding!
Ellen

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About the Author

I’m Ellen, a Machine Learning engineer with 6 years of experience, currently working at a fintech startup in San Francisco. My background spans data science roles in oil & gas consulting, as well as leading AI and data training programs across APAC, the Middle East, and Europe.

I’m currently completing my Master’s in Data Science (graduating May 2025) and actively looking for my next opportunity as a machine learning engineer. If you’re open to referring or connecting, I’d truly appreciate it!

I love creating real-world impact through AI and I’m always open to project-based collaborations as well.

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Noticias

Lo que dice el acuerdo de OpenAI del Washington Post sobre las licencias de IA

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  • Los primeros 100 días de Trump luchando contra la prensa, el cambio de los medios de comunicación a los videos de podcasts y más.
  • La evolución de la licencia de contenido de IA ofertas

    El Washington Post se ha convertido en el último editor importante en llegar a un acuerdo de licencia con Openai, uniéndose a una cohorte creciente que ahora abarca más de 20 organizaciones de noticias.

    Es parte de un patrón familiar: cada pocos meses, Openai bloquea otro editor para reforzar su tubería de contenido. Pero los términos de estos acuerdos parecen estar evolucionando en silencio, alejándose sutilmente del lenguaje explícito en torno a los datos de capacitación que definieron acuerdos anteriores y planteando nuevas preguntas sobre lo que ahora significan estas asociaciones.

    El acuerdo del Washington Post se centra en surgir su contenido en respuesta a consultas relacionadas con las noticias. “Como parte de esta asociación, ChatGPT mostrará resúmenes, citas y enlaces a informes originales de la publicación en respuesta a preguntas relevantes”, se lee el anuncio el 22 de abril sobre el acuerdo de la publicación con OpenAI. En contraste, el pasado se ocupa de editores como Axel Springer y Time, firmado en diciembre de 2023 y junio de 2024 respectivamente, explícitamente incluyó disposiciones para la capacitación de LLM de OpenAI en su contenido.

    El acuerdo de OpenAI de The Guardian, anunciado en febrero de 2025, tiene una redacción similar al anuncio del Washington Post y no se menciona los datos de capacitación. Un portavoz de Guardian se negó a comentar sobre los términos de acuerdo con OpenAI. El Washington Post no respondió a las solicitudes de comentarios.

    Estos cambios algo sutiles en el lenguaje de los términos podrían indicar un cambio más amplio en el paisaje de IA, según conversaciones con cuatro Expertos legales de medios. Podría indicar un cambio en cómo los acuerdos de licencia de contenido de IA están estructurados en el futuro, con más editores que potencialmente buscan acuerdos que prioricen la atribución y la prominencia en los motores de búsqueda de IA sobre los derechos para la capacitación modelo.

    Otro factor a tener en cuenta: estas compañías de IA ya han capacitado a sus LLM en grandes cantidades de contenido disponible en la web, según Aaron Rubin, socio del grupo estratégico de transacciones y licencias en la firma de abogados Gunderson Dettmer. Y debido a que las compañías de IA enfrentan litigios de compañías de medios que afirman que esto era una infracción de derechos de autor, como el caso del New York Times contra OpenAI, si las compañías de IA continuaran pagando a los datos de licencia con fines de capacitación, podría verse como “una admisión implícita” que debería haber pagado para licenciar esos datos y no haberlo escrito de forma gratuita, dijo Rubin.

    “[AI companies] Ya tienen un billón de palabras que han robado. No necesitan las palabras adicionales tan mal para la capacitación, pero quieren tener el contenido actualizado para respuestas [in their AI search engines]”, Dijo Bill Gross, fundador de la empresa de inicio de IA Prorata.ai, que está construyendo soluciones tecnológicas para compensar a los editores por el contenido utilizado por las compañías generativas de IA.

    Tanto las compañías de IA como los editores pueden beneficiarse de esta posible evolución, según Rubin. Las compañías de IA obtienen acceso a noticias confiables y actualizadas de fuentes confiables para responder preguntas sobre los eventos actuales en sus productos, y los editores “pueden llenar un vacío que tenían miedo que faltaran con la forma en que estas herramientas de IA han evolucionado. Estaban perdiendo clics y globos oculares y enlaces a sus páginas”, dijo. Tener una mejor atribución en lugares como la búsqueda de chatgpt tiene el potencial de impulsar más tráfico a los sitios de los editores. Al menos, esa es la esperanza.

    “Tiene el potencial de generar más dinero para los editores”, dijo Rubin. “Los editores están apostando a que así es como las personas van a interactuar con los medios de comunicación en el futuro”.

    Desde el otoño pasado, Operai ha desafiado a los gigantes de búsqueda como Google con su motor de búsqueda de IA, búsqueda de chatgpt, y ese esfuerzo depende del acceso al contenido de noticias. Cuando se le preguntó si la estructura de los acuerdos de Operai con los editores había cambiado, un portavoz de OpenAI señaló el lanzamiento de la compañía de la compañía de ChatGPT en octubre de 2024, así como mejoras anunciadas esta semana.

    “Tenemos un feed directo al contenido de nuestro socio editor para mostrar resúmenes, citas y enlaces atribuidos a informes originales en respuesta a preguntas relevantes”, dijo el portavoz. “Ese es un componente de las ofertas. La capacitación posterior ayuda a aumentar la precisión de las respuestas relacionadas con el contenido de un editor”. El portavoz no respondió a otras solicitudes de comentarios.

    No está claro cuántos editores como The Washington Post no se pueden hacer de OpenAI, especialmente porque puede surgir un modelo diferente centrado en la búsqueda de ChatGPT. Pero la perspectiva para los acuerdos de licencia entre editores y compañías de IA parece estar empeorando. El valor de estos acuerdos está “en picado”, al menos según el CEO de Atlantic, Nicholas Thompson, quien habló en el evento Reuters Next en diciembre pasado.

    “Todavía hay un mercado para la licencia de contenido para la capacitación y eso sigue siendo importante, pero continuaremos viendo un enfoque en entrar en acuerdos que resultan en impulsar el tráfico a los sitios”, dijo John Monterubio, socio del grupo avanzado de medios y tecnología en la firma de abogados Loeb & Loeb. “Será la nueva forma de marketing de SEO y compra de anuncios, para parecer más altos en los resultados al comunicarse con estos [generative AI] herramientas.”

    Lo que hemos escuchado

    “No tenemos que preocuparnos por una narración algo falsa de: las cookies deben ir … entonces puedes poner todo este ancho de banda y potencia para mejorar el mercado actual, sin preocuparte por un posible problema futuro que estuviera en el control de Google todo el tiempo”.

    Anónimo Publishing Ejecute la decisión de Google la semana pasada de continuar usando cookies de terceros en Chrome.

    Números para saber

    $ 50 millones: la cantidad que Los Angeles Times perdió en 2024.

    50%: El porcentaje de adultos estadounidenses que dijeron que la IA tendrá un impacto muy o algo negativo en las noticias que las personas obtienen en los EE. UU. Durante los próximos 20 años, según un estudio del Centro de Investigación Pew.

    $ 100 millones: la cantidad Spotify ha pagado a los editores y creadores de podcasts desde enero.

    0.3%: La disminución esperada en el uso de los medios (canales digitales y tradicionales) en 2025, la primera caída desde 2009, según PQ Media Research.

    Lo que hemos cubierto

    Las demandas de AI destacan las luchas de los editores para impedir que los bots raspen contenido

    • La reciente demanda de Ziff Davis contra Operai destaca la realidad de que los editores aún no tienen una forma confiable de evitar que las compañías de IA raspen su contenido de forma gratuita.
    • Si bien han surgido herramientas como Robots.txt archivos, paredes de pago y etiquetas de bloqueo AI-AI, muchos editores admiten que es muy difícil hacer cumplir el control en cada bot, especialmente porque algunos ignoran los protocolos estándar o enmascaran sus identidades.

    Leer más aquí.

    ¿Quién compraría Chrome?

    • El ensayo antimonopolio de búsqueda de Google podría obligar a Google a separarse del navegador Chrome.
    • Si lo hizo, OpenAi, Perplexity, Yahoo y Duckduckgo podrían ser algunos de los compradores potenciales.

    Lea más sobre el impacto potencial de una venta masiva de Chrome aquí.

    Tiktok está cortejando a los creadores y agencias para participar en sus herramientas en vivo

    • Tiktok está tratando de demostrar el potencial de ingresos de sus herramientas en vivo.
    • La plataforma de redes sociales dice que sus creadores ahora generan colectivamente $ 10 millones en ingresos diariamente a través de la transmisión en vivo.

    Lea más sobre el tono de Tiktok aquí.

    ¿WTF son bots grises?

    • Los rastreadores y raspadores de IA generativos están siendo llamados “bots grises” por algunos para ilustrar la línea borrosa entre el tráfico real y falso.
    • Estos bots pueden afectar el análisis y robar contenido, y las impresiones publicitarias impulsadas por la IA pueden dañar las tasas de clics y las tasas de conversión.

    Lea más sobre por qué los bots grises son un riesgo para los editores aquí.

    ¿Facebook se está convirtiendo en un nuevo flujo de ingresos nuevamente para los editores?

    • Los editores han sido testigos de un reciente pico de referencia de Facebook, y es, algo sorprendentemente, coincidiendo con una afluencia de ingresos del programa de monetización de contenido de Meta.
    • De los 10 editores con los que Digay habló para este artículo, varios están en camino de hacer entre seis y siete cifras este año del último programa de monetización de contenido de Meta.

    Lea más sobre lo que reciben los editores de Facebook aquí.

    Lo que estamos leyendo

    Las ambiciones de video de los podcasts de los medios de comunicación destacan el movimiento del formato de audio a la televisión

    Los medios de comunicación como el New York Times y el Atlantic están poniendo más recursos en la producción de videos de los populares programas de podcast para aprovechar el público más joven de YouTube, informó Vanity Fair.

    La perplejidad quiere recopilar datos sobre los usuarios para vender anuncios personalizados

    El CEO de Perplexity, Aravind Srinivas, dijo que la perplejidad está construyendo su propio navegador para recopilar datos de usuarios y vender anuncios personalizados, informó TechCrunch.

    El presidente Trump apunta a la prensa en los primeros 100 días

    El presidente Trump apunta a las compañías de medios tradicionales en sus primeros 100 días, utilizando tácticas como prohibir los puntos de venta de que cubren los eventos de la Casa Blanca hasta el lanzamiento de investigaciones en las principales redes, informó Axios.

    SemAFOR probará suscripciones

    SemaFor “probará” suscripciones en “Due Time”, el fundador Justin Smith dijo al Inteligencer de la revista New York en una inmersión profunda en la empresa de inicio de noticias centrada en el boletín.

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