Welcome to HackerNoon’s Writing Prompts! Would you like to take a stab at answering some of these questions? The link for the template is HERE. Welcome to HackerNoon’s Writing Prompts! Would you like to take a stab at answering some of these questions? The link for the template is HERE. HackerNoon The link for the template is HERE. 1. What is your company in 2–5 words? Denis Alaev: EODHD APIs is a top-rated financial data provider. Denis Alaev: Denis Alaev: Denis Alaev top-rated top-rated 2. Why is now the time for your company to exist? The demand for reliable and affordable financial data is higher than ever. As more businesses and individuals rely on data-driven decision-making, there's a growing need for accessible APIs that deliver accurate financial data without the hefty costs of traditional providers. 3. What do you love about your team, and why are you the ones to solve this problem? Our team combines deep technical expertise with product development experience from international tech companies. We understand both the technical challenges of financial data and the user experience side, which makes us uniquely positioned to build a product that's both robust and easy to use. 4. If you weren’t building your startup, what would you be doing? I’d likely still be building products — either in big tech or a fast-paced startup. With my background in computer science and product management, I'd continue solving technical problems that matter. 5. At the moment, how do you measure success? What are your metrics? At the moment, we measure success primarily by the number of active users — we currently have tens of thousands, which is our key product metric. Additionally, we closely monitor customer satisfaction through our Trustpilot rating and user feedback. Positive reviews and high satisfaction scores are strong indicators that we’re delivering real value and meeting user expectations. 6. In a few sentences, what do you offer to whom? We provide high-quality, affordable APIs for historical and fundamental financial market data. Our customers range from individual investors and developers to fintech companies and startups looking to integrate clean and accurate financial data into their products. 7. What’s most exciting about your traction to date? What excites us most is how quickly we grew after pivoting from a B2C investment analysis platform to a B2B API-first company. We are bootstrapped, have reached a six-figure MRR and built a loyal client base by solving a real market pain point. 8. Where do you think your growth will be next year? We expect growth to come from expanding our data sources, improving API performance, and reaching new markets — especially in regions underserved by traditional financial data providers. In addition, we see strong growth potential through the integration of AI into our services, particularly in data checking and validation. This will not only improve data quality but also enable us to offer new, intelligent services that better meet our users' needs. 9. Tell us about your first paying customer and revenue expectations over the next year. Our first paying customer signed up during the launch weekend after we built a simple landing page. It was an early sign that we were solving a real problem. Based on our current trajectory, we anticipate sustained revenue growth throughout the next year. 10. What’s your biggest challenge? Our biggest challenge is managing the transition from a small, fast-moving team to a growing organization with evolving processes. As we scale, things change rapidly — which requires us to stay agile, maintain control, and constantly anticipate what’s coming next. We need to combine strong operational discipline with visionary thinking to stay ahead of market shifts and external events. For example, when the market is booming, subscriber numbers grow. But when the market slows down or crashes, interest in trading often declines due to psychological factors — people lose confidence or become discouraged. Navigating these fluctuations and keeping users engaged through both market highs and lows is a constant challenge we’re focused on. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke’s ten questions for startup founders.
Would you like to take a stab at answering some of these questions? The link for the template is HERE. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke’s ten questions for startup founders. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke’s ten questions for startup founders. HackerNoon Founder & CEO David Smooke ten questions for startup founders Would you like to take a stab at answering some of these questions? The link for the template is HERE. The link for the template is HERE.

How EODHD APIs Quietly Built a Six-Figure Business by Fixing Financial Data

Like many students, I do not enjoy scrolling through endless PDFs. I would if I could skip reading and just ask my textbook questions. So, naturally, I did what any lazy-but-resourceful person would do: I dumped the entire PDF into an LLM and started asking questions, praying to God that the answers were accurate. do not Spoiler alert: they weren’t. The answers were either vague, wrong, or just plain confusing. That’s when I realized — large language models aren’t magic (shocking, I know). They have context limits, and stuffing a whole book into one prompt is like trying to fit a watermelon into a ziplock bag. context limits So I started digging, and that’s when I found the real MVP: RAG (Retrieval-Augmented Generation). With RAG, instead of force-feeding the model everything, you teach it where to look — and suddenly, answers start making sense RAG (Retrieval-Augmented Generation) where Why Large Context Windows Don’t Really Help (Much) Why Large Context Windows Don’t Really Help (Much) You might think, “Wait… but newer models have massive context windows, right? Shouldn’t that fix the problem?” In theory? Yes. In practice? Meh. Even with context windows stretching up to 100k tokens (which sounds huge), you're still working with trade-offs: They’re expensive to use.


They often truncate or compress information.


And unless your prompt is perfectly structured (which is rarely the case), the model still ends up hallucinating or giving generic responses. They’re expensive to use. They’re expensive to use. They often truncate or compress information. They often truncate or compress information. And unless your prompt is perfectly structured (which is rarely the case), the model still ends up hallucinating or giving generic responses. And unless your prompt is perfectly structured (which is rarely the case), the model still ends up hallucinating or giving generic responses. It’s like asking your friend to remember every word of a 300-page book and hoping they don’t mess up the details. Not ideal. RAG to the Rescue RAG to the Rescue RAG — Retrieval-Augmented Generation — is like giving your LLM a cheat sheet… but a really smart, targeted one. RAG — Retrieval-Augmented Generation — is like giving your LLM a cheat sheet… but a really smart, targeted one. Here’s the flow: Here’s the flow: You split your book into smaller chunks
You store these chunks in a vector DB.
When a user asks a question, you don’t give the model the entire book — just the most relevant parts.
Then the LLM crafts a solid, informed answer using onlythose parts. You split your book into smaller chunks You store these chunks in a vector DB. When a user asks a question, you don’t give the model the entire book — just the most relevant parts. entire most relevant parts Then the LLM crafts a solid, informed answer using onlythose parts. only Less noise. More signal. Way better answers. What Does the RAG Pipeline Look Like? What Does the RAG Pipeline Look Like? Imagine you’re the middleman between your textbook and your model. Your job is to: Your job is to: Split the content → Break the book into readable chunks


Convert them into vectors → Using an embedding model (Cohere)


Save those vectors → In a vector database (Pinecone)


When a question is asked:

Convert the question into a vector
Search the database for chunks that are most similar (using cosine distance metric)
Send the best matches + the question to a language model (I used Gemini)
Boom — you get a clear, helpful answer Split the content → Break the book into readable chunks Split the content → Break the book into readable chunks Convert them into vectors → Using an embedding model (Cohere) Convert them into vectors → Using an embedding model (Cohere) Save those vectors → In a vector database (Pinecone) Save those vectors → In a vector database (Pinecone) When a question is asked:

Convert the question into a vector
Search the database for chunks that are most similar (using cosine distance metric)
Send the best matches + the question to a language model (I used Gemini)
Boom — you get a clear, helpful answer When a question is asked: Convert the question into a vector
Search the database for chunks that are most similar (using cosine distance metric)
Send the best matches + the question to a language model (I used Gemini)
Boom — you get a clear, helpful answer Convert the question into a vector Search the database for chunks that are most similar (using cosine distance metric) Send the best matches + the question to a language model (I used Gemini) Boom — you get a clear, helpful answer And that’s the heart of it. You’re not replacing the model’s brain — just giving it better memory. My Stack: Simple, Powerful, Beginner-Friendly My Stack: Simple, Powerful, Beginner-Friendly Here’s what I used: 🧠 Cohere – To turn both book content and questions into vectors (aka embeddings)
📦 Pinecone – To store and search those vectors super efficiently
💬 Gemini – To generate the final, natural-language response 🧠 Cohere – To turn both book content and questions into vectors (aka embeddings) 🧠 Cohere – 📦 Pinecone – To store and search those vectors super efficiently 📦 Pinecone – 💬 Gemini – To generate the final, natural-language response 💬 Gemini – You don’t have to use these, but this combo is beginner-friendly, well-documented, and plays nicely together. have Step-by-Step: Build Your Own AskMyBook Bot Step-by-Step: Build Your Own AskMyBook Bot Okay, let’s actually build the thing now. I used Google Colab (because free GPU and easy sharing), but this should work in any Python environment. Okay, let’s actually build the thing now. I used Google Colab (because free GPU and easy sharing), but this should work in any Python environment. Step 1: Load and Chunk Your Book Step 1: Load and Chunk Your Book I used the PyMuPDF library to extract text. I used the PyMuPDF library to extract text. !pip install pymupdf !pip install pymupdf Now, let’s extract the text: Now, let’s extract the text: import fitz  # PyMuPDF

def extract_text_from_pdf(pdf_path):

    doc = fitz.open(pdf_path)

    text = ""

    for page in doc:

        text += page.get_text()

    return text 

book_path = 'enter the path here'

book_text = extract_text_from_pdf(book_path) import fitz  # PyMuPDF

def extract_text_from_pdf(pdf_path):

    doc = fitz.open(pdf_path)

    text = ""

    for page in doc:

        text += page.get_text()

    return text 

book_path = 'enter the path here'

book_text = extract_text_from_pdf(book_path) Now, we’ll split the book into chunks, making it more digestible. import re

def chunk_text(text, chunk_size=300, overlap=50):

    words = re.findall(r'\S+', text)

    chunks = []

    for i in range(0, len(words), chunk_size - overlap):

        chunk = ' '.join(words[i:i + chunk_size])

        chunks.append(chunk)

    return chunks

chunks = chunk_text(book_text)

print(f"Total Chunks: {len(chunks)}")

print("Sample chunk:\n", chunks[0]) import re

def chunk_text(text, chunk_size=300, overlap=50):

    words = re.findall(r'\S+', text)

    chunks = []

    for i in range(0, len(words), chunk_size - overlap):

        chunk = ' '.join(words[i:i + chunk_size])

        chunks.append(chunk)

    return chunks

chunks = chunk_text(book_text)

print(f"Total Chunks: {len(chunks)}")

print("Sample chunk:\n", chunks[0]) Here, each chunk has 300 words, with a 50-word overlap for context continuity. Think of it as giving the model a smooth flow between paragraphs. Step 2: Create Embeddings with Cohere Embeddings = turning text into numbers that reflect meaning. We’ll use Cohere's embed-english-v3.0 model for this. !pip install cohere

import cohere

co = cohere.Client("YOUR-API-KEY")  # Replace with your actual key

def get_embeddings(texts):

    response = co.embed(

        texts=texts,

        model="embed-english-v3.0",

        input_type="search_document"

    )

    return response.embeddings !pip install cohere

import cohere

co = cohere.Client("YOUR-API-KEY")  # Replace with your actual key

def get_embeddings(texts):

    response = co.embed(

        texts=texts,

        model="embed-english-v3.0",

        input_type="search_document"

    )

    return response.embeddings Step 3: Store Chunks in Pinecone Now we store the embeddings in Pinecone — a vector database that helps us search similar chunks later. !pip install pinecone

import pinecone

pinecone = pinecone.Pinecone(api_key="YOUR-API-KEY")

index_name = "ask-my-book"

if index_name not in pinecone.list_indexes().names():

    pinecone.create_index(

        index_name,

        spec={

            "pod": {

                "pod_type": "p1",

                "replicas": 1,

                "metric": "cosine",

                "environment": "aws-us-east1"

            }

        },

        dimension=1024

    )

index = pinecone.Index(index_name) !pip install pinecone

import pinecone

pinecone = pinecone.Pinecone(api_key="YOUR-API-KEY")

index_name = "ask-my-book"

if index_name not in pinecone.list_indexes().names():

    pinecone.create_index(

        index_name,

        spec={

            "pod": {

                "pod_type": "p1",

                "replicas": 1,

                "metric": "cosine",

                "environment": "aws-us-east1"

            }

        },

        dimension=1024

    )

index = pinecone.Index(index_name) Now, batch upload the chunks import uuid

import time

batch_size = 96 

for i in range(0, len(chunks), batch_size):

    batch_chunks = chunks[i:i+batch_size]

    batch_embeds = get_embeddings(batch_chunks)

    ids = [str(uuid.uuid4()) for _ in batch_chunks]

    vectors = list(zip(ids, batch_embeds, [{"text": t} for t in batch_chunks]))

    index.upsert(vectors=vectors)

    time.sleep(60)  # avoid hitting rate limits import uuid

import time

batch_size = 96 

for i in range(0, len(chunks), batch_size):

    batch_chunks = chunks[i:i+batch_size]

    batch_embeds = get_embeddings(batch_chunks)

    ids = [str(uuid.uuid4()) for _ in batch_chunks]

    vectors = list(zip(ids, batch_embeds, [{"text": t} for t in batch_chunks]))

    index.upsert(vectors=vectors)

    time.sleep(60)  # avoid hitting rate limits Boom! Your book is now smartly stored in vector format. Step 4: Ask Questions + Get Answers with Gemini We’ll search for relevant chunks using your query, and then pass those to Gemini for generating an answer. First, get the query embedding: def get_query_embedding(query):

    response = co.embed(

        texts=[query],

        model="embed-english-v3.0",

        input_type="search_query"

    )

    return response.embeddings[0] def get_query_embedding(query):

    response = co.embed(

        texts=[query],

        model="embed-english-v3.0",

        input_type="search_query"

    )

    return response.embeddings[0] Now, search Pinecone: def search_similar_chunks(query, top_k=5):

    query_embedding = get_query_embedding(query)

    result = index.query(vector=query_embedding, top_k=top_k, include_metadata=True)

    return [match['metadata']['text'] for match in result['matches']] def search_similar_chunks(query, top_k=5):

    query_embedding = get_query_embedding(query)

    result = index.query(vector=query_embedding, top_k=top_k, include_metadata=True)

    return [match['metadata']['text'] for match in result['matches']] Then plug the top chunks into Gemini: import google.generativeai as genai

genai.configure(api_key="YOUR-GEMINI-API-KEY")

model = genai.GenerativeModel("gemini-1.5-flash")

def generate_answer(query):

    context_chunks = search_similar_chunks(query)

    context = "\n\n".join(context_chunks)

    prompt = f"""

You are an assistant bot trained on the following book content. Use only the info provided to answer the user's question.

Book Context:

{context}

Question:

{query}

If the question is not relevant to the context, respond with:

'I am a bot trained to answer questions based on the book content. This question is out of scope.'

"""

    response = model.generate_content(prompt)

    return response.text import google.generativeai as genai

genai.configure(api_key="YOUR-GEMINI-API-KEY")

model = genai.GenerativeModel("gemini-1.5-flash")

def generate_answer(query):

    context_chunks = search_similar_chunks(query)

    context = "\n\n".join(context_chunks)

    prompt = f"""

You are an assistant bot trained on the following book content. Use only the info provided to answer the user's question.

Book Context:

{context}

Question:

{query}

If the question is not relevant to the context, respond with:

'I am a bot trained to answer questions based on the book content. This question is out of scope.'

"""

    response = model.generate_content(prompt)

    return response.text Try it out! question = "What’s does the author say in Module 1 of the book?"

print(generate_answer(question)) question = "What’s does the author say in Module 1 of the book?"

print(generate_answer(question)) That’s It — You Now Have an Ask-My-Book Bot! You built a bot that: Understands your textbook
Finds the right part when asked
Gives meaningful answers using that part only Understands your textbook Finds the right part when asked Gives meaningful answers using that part only No more endless skimming. Just type and ask. What Next? Level Up Your Book-Bot What we’ve built is a basic but powerful Question-and-Answer system. Think of it as the MVP (Minimum Viable Product) of your personal study assistant. But once you’re comfortable, there’s so much more you can add: Citations – Show which chunk or page the answer came from, so you can verify the source.
Multi-turn Conversations – Let the bot remember previous questions and give more intelligent answers over time.
Multi-step Reasoning – Chain thoughts together to answer complex questions.
Custom Memory – Let your bot hold on to important facts you highlight for future queries.
UI Upgrade – Hook this into a Streamlit or React frontend for a polished, user-friendly experience. Citations – Show which chunk or page the answer came from, so you can verify the source. Multi-turn Conversations – Let the bot remember previous questions and give more intelligent answers over time. Multi-step Reasoning – Chain thoughts together to answer complex questions. Custom Memory – Let your bot hold on to important facts you highlight for future queries. UI Upgrade – Hook this into a Streamlit or React frontend for a polished, user-friendly experience. With these, your bot goes from “smart textbook” to “AI study buddy.” If you’ve ever stared at a textbook, praying it would just talk back and tell you what matters — well, now it can. talk back This was my little experiment in turning boring PDFs into interactive conversations. Hope it inspires you to build your own and maybe even customize it for friends or classes. Got stuck somewhere? Want help with adding a UI or citations next? Drop a comment or ping me — always happy to chat.

Tired of Digging Through Long PDFs? You Can Build a Bot That Can Quickly Answer Questions for You

What Are Event-Driven Systems? An event-driven system is an architecture where components react to events rather than waiting for direct requests. In contrast to traditional synchronous systems, where services make blocking calls and wait for responses, event-driven architectures allow services to communicate asynchronously via event notifications. react to events rather than waiting for direct requests Key Components of Event-Driven Systems Event Producers – Generate events when actions occur (e.g., a user skips a song).
Event Routers – Deliver events to interested subscribers (e.g., SNS, EventBridge).
Event Consumers – Process events and take necessary actions (e.g., SQS, Lambda, database updates). Event Producers – Generate events when actions occur (e.g., a user skips a song). Event Routers – Deliver events to interested subscribers (e.g., SNS, EventBridge). Event Consumers – Process events and take necessary actions (e.g., SQS, Lambda, database updates). How Are They Different from Synchronous Systems? Feature

Synchronous Systems

Event-Driven (Asynchronous) Systems



Processing

Request-response model (blocking)

Event-based (non-blocking)



Latency

Users wait for responses

Immediate response while processing happens in the background



Fault Tolerance

Failures propagate through services

Failures are isolated from other processes Feature

Synchronous Systems

Event-Driven (Asynchronous) Systems



Processing

Request-response model (blocking)

Event-based (non-blocking)



Latency

Users wait for responses

Immediate response while processing happens in the background



Fault Tolerance

Failures propagate through services

Failures are isolated from other processes Feature

Synchronous Systems

Event-Driven (Asynchronous) Systems Feature Feature Feature Synchronous Systems Synchronous Systems Synchronous Systems Event-Driven (Asynchronous) Systems Event-Driven (Asynchronous) Systems Event-Driven (Asynchronous) Systems Processing

Request-response model (blocking)

Event-based (non-blocking) Processing Processing Processing Request-response model (blocking) Request-response model (blocking) Event-based (non-blocking) Event-based (non-blocking) Latency

Users wait for responses

Immediate response while processing happens in the background Latency Latency Latency Users wait for responses Users wait for responses Immediate response while processing happens in the background Immediate response while processing happens in the background Fault Tolerance

Failures propagate through services

Failures are isolated from other processes Fault Tolerance Fault Tolerance Fault Tolerance Failures propagate through services Failures propagate through services Failures are isolated from other processes Failures are isolated from other processes When Should Event-Driven Systems Be Used? As the title suggests, the obvious use cases are for systems that rely primarily on events (For example a package delivery tracker). However the key use case I want to focus on here is to facilitate expensive asynchronous data processing to allow for low latency request based retrieval. Explained Using an Example: Personalized Playlists What are Personalised Playlists? Spotify continuously updates personalized playlists (like Discover Weekly) based on user interactions. For example, when a user skips/likes a song, that data is processed asynchronously to update their music preferences. Discover Weekly A synchronous system would try to generate a playlist on request.. Instead, an event-driven system ensures: Fast User Experience – The UI does not wait for processing.
Efficient Processing – Large-scale data updates happen asynchronously.
Scalability – Millions of users can trigger song events without overwhelming the system. Fast User Experience – The UI does not wait for processing. Efficient Processing – Large-scale data updates happen asynchronously. Scalability – Millions of users can trigger song events without overwhelming the system. Personalized playlist Event-Driven Architecture Using AWS Such architecture would work as follows: Step 1: The Event Producer – User Skips/likes a Song When a user skips or likes a song in the Spotify app, an event (SongSkipped, SongLiked) is generated.
This event is published to Amazon SNS (Simple Notification Service), which acts as an event router. When a user skips or likes a song in the Spotify app, an event (SongSkipped, SongLiked) is generated. This event is published to Amazon SNS (Simple Notification Service), which acts as an event router. Step 2: Event Routing – SNS Distributes the Event SNS broadcasts the event to multiple subscribers:

SQS Queue (for batch processing of user preferences).
AWS Lambda (for real-time metrics and analytics updates).
EventBridge (if needed for integration with third-party recommendation models). SNS broadcasts the event to multiple subscribers:

SQS Queue (for batch processing of user preferences).
AWS Lambda (for real-time metrics and analytics updates).
EventBridge (if needed for integration with third-party recommendation models). SQS Queue (for batch processing of user preferences).
AWS Lambda (for real-time metrics and analytics updates).
EventBridge (if needed for integration with third-party recommendation models). SQS Queue (for batch processing of user preferences). AWS Lambda (for real-time metrics and analytics updates). EventBridge (if needed for integration with third-party recommendation models). Step 3: Event Consumer – Processing in SQS and Lambda Amazon SQS (Simple Queue Service) receives the event and stores it in a queue. Note multiple queues can subscribe to an SNS event.


A personalization microservice (running in AWS Fargate or Lambda) pulls events from the SQS queue and processes them:

Updates the User Preferences Store in Amazon DynamoDB.
Updates an aggregated machine learning model that improves recommendations.
The machine learning model re generates the playlist with new information and stores it in DynamoDb



A user activity lambda listens through its own queue (subscribed to multiple activity topics) and updates the activity table


A playlist preview service listens to DynamoDB stream events and saves some playlists in ElastiCache with song previews Amazon SQS (Simple Queue Service) receives the event and stores it in a queue. Note multiple queues can subscribe to an SNS event. Amazon SQS (Simple Queue Service) receives the event and stores it in a queue. Note multiple queues can subscribe to an SNS event. A personalization microservice (running in AWS Fargate or Lambda) pulls events from the SQS queue and processes them:

Updates the User Preferences Store in Amazon DynamoDB.
Updates an aggregated machine learning model that improves recommendations.
The machine learning model re generates the playlist with new information and stores it in DynamoDb A personalization microservice (running in AWS Fargate or Lambda) pulls events from the SQS queue and processes them: Updates the User Preferences Store in Amazon DynamoDB.
Updates an aggregated machine learning model that improves recommendations.
The machine learning model re generates the playlist with new information and stores it in DynamoDb Updates the User Preferences Store in Amazon DynamoDB. Updates an aggregated machine learning model that improves recommendations. The machine learning model re generates the playlist with new information and stores it in DynamoDb A user activity lambda listens through its own queue (subscribed to multiple activity topics) and updates the activity table A user activity lambda listens through its own queue (subscribed to multiple activity topics) and updates the activity table A playlist preview service listens to DynamoDB stream events and saves some playlists in ElastiCache with song previews A playlist preview service listens to DynamoDB stream events and saves some playlists in ElastiCache with song previews Step 4: Real-Time API Call for Updated Playlists When the user later requests a playlist, the Spotify API fetches recommendations from:

ElastiCache (Redis) – Fast retrieval of recommendations with previews
DynamoDB (if Redis cache is expired).



The user instantly sees an updated personalized playlist based on their past interactions. When the user later requests a playlist, the Spotify API fetches recommendations from:

ElastiCache (Redis) – Fast retrieval of recommendations with previews
DynamoDB (if Redis cache is expired). When the user later requests a playlist, the Spotify API fetches recommendations from: ElastiCache (Redis) – Fast retrieval of recommendations with previews
DynamoDB (if Redis cache is expired). ElastiCache (Redis) – Fast retrieval of recommendations with previews DynamoDB (if Redis cache is expired). The user instantly sees an updated personalized playlist based on their past interactions. The user instantly sees an updated personalized playlist based on their past interactions. AWS Services Used and Their Role AWS Service

Role in Architecture



Amazon SNS

Publishes and routes the events (ex: SongSkipped) to multiple subscribers.



Amazon SQS

Stores events for batch processing, ensuring scalability.



AWS Lambda

Processes real-time analytics updates and sends metrics.



Amazon DynamoDB

Stores user preferences and recommendation history.



Amazon ElastiCache (Redis)

Provides low-latency playlist retrieval for API calls.



Amazon SageMaker

Hosts and runs machine learning models



AWS Fargate

Compute platform for heavy logic microservices



AWS S3

Cold storage for large data (music files) AWS Service

Role in Architecture



Amazon SNS

Publishes and routes the events (ex: SongSkipped) to multiple subscribers.



Amazon SQS

Stores events for batch processing, ensuring scalability.



AWS Lambda

Processes real-time analytics updates and sends metrics.



Amazon DynamoDB

Stores user preferences and recommendation history.



Amazon ElastiCache (Redis)

Provides low-latency playlist retrieval for API calls.



Amazon SageMaker

Hosts and runs machine learning models



AWS Fargate

Compute platform for heavy logic microservices



AWS S3

Cold storage for large data (music files) AWS Service

Role in Architecture AWS Service AWS Service AWS Service Role in Architecture Role in Architecture Role in Architecture Amazon SNS

Publishes and routes the events (ex: SongSkipped) to multiple subscribers. Amazon SNS Amazon SNS Publishes and routes the events (ex: SongSkipped) to multiple subscribers. Publishes and routes the events (ex: SongSkipped) to multiple subscribers. Amazon SQS

Stores events for batch processing, ensuring scalability. Amazon SQS Amazon SQS Stores events for batch processing, ensuring scalability. Stores events for batch processing, ensuring scalability. AWS Lambda

Processes real-time analytics updates and sends metrics. AWS Lambda AWS Lambda Processes real-time analytics updates and sends metrics. Processes real-time analytics updates and sends metrics. Amazon DynamoDB

Stores user preferences and recommendation history. Amazon DynamoDB Amazon DynamoDB Stores user preferences and recommendation history. Stores user preferences and recommendation history. Amazon ElastiCache (Redis)

Provides low-latency playlist retrieval for API calls. Amazon ElastiCache (Redis) Amazon ElastiCache (Redis) Provides low-latency playlist retrieval for API calls. Provides low-latency playlist retrieval for API calls. Amazon SageMaker

Hosts and runs machine learning models Amazon SageMaker Amazon SageMaker Hosts and runs machine learning models Hosts and runs machine learning models AWS Fargate

Compute platform for heavy logic microservices AWS Fargate AWS Fargate Compute platform for heavy logic microservices Compute platform for heavy logic microservices AWS S3

Cold storage for large data (music files) AWS S3 AWS S3 Cold storage for large data (music files) Cold storage for large data (music files) Advantages of this Architecture Instant User Experience – Playlists update asynchronously without blocking requests.
Scalable Event Processing – SNS and SQS handle millions of concurrent events and event bursts. SQS can decide the queue polling frequency to ensure the backend systems have an even load.
Optimized Costs – AWS Lambda and Fargate scale based on demand, reducing idle costs.
Decoupled Services – Microservices can evolve independently. Instant User Experience – Playlists update asynchronously without blocking requests. Scalable Event Processing – SNS and SQS handle millions of concurrent events and event bursts. SQS can decide the queue polling frequency to ensure the backend systems have an even load. Optimized Costs – AWS Lambda and Fargate scale based on demand, reducing idle costs. Decoupled Services – Microservices can evolve independently. Disadvantages of Event-Driven Systems While event-driven systems provide scalability and flexibility, they have some trade-offs: Complex Debugging & Monitoring – Events flow asynchronously, making debugging harder.
Event Ordering Challenges – Handling event order across distributed services can be tricky. Events may come in outside expected orders and you must account for how to handle them.
Increased Latency for Data Consistency – Updates propagate asynchronously, leading to eventual consistency instead of immediate consistency.
Error Handling Complexity – Failed events require dead-letter queues (DLQs) to prevent message loss. Redriving dead letter queues also has challenges since these events are no longer accurate representations of the time they actually occurred. Complex Debugging & Monitoring – Events flow asynchronously, making debugging harder. Event Ordering Challenges – Handling event order across distributed services can be tricky. Events may come in outside expected orders and you must account for how to handle them. Increased Latency for Data Consistency – Updates propagate asynchronously, leading to eventual consistency instead of immediate consistency. Error Handling Complexity – Failed events require dead-letter queues (DLQs) to prevent message loss. Redriving dead letter queues also has challenges since these events are no longer accurate representations of the time they actually occurred. Despite these challenges, using AWS monitoring tools like AWS X-Ray, CloudWatch, and SQS DLQs can mitigate many risks. Good logging and metrics are paramount to be able to audit and understand what happened and why. Conclusion Event-driven architectures allow platforms to process user interactions at scale while ensuring real-time API responses. By leveraging AWS services such as SNS, SQS, DynamoDB, Lambda, and ElastiCache, Systems can asynchronously update recommendations without affecting user experience. This loose coupling of services ensures high availability, cost efficiency, and scalability, making event-driven systems the preferred choice for large-scale applications. Would you adopt an event-driven approach in your applications? Let me know your thoughts! References Official AWS documentation: SNS: https://aws.amazon.com/sns/
SQS: https://aws.amazon.com/sqs/
Lambda: https://aws.amazon.com/lambda/
SageMaker: https://aws.amazon.com/sagemaker/ SNS: https://aws.amazon.com/sns/ https://aws.amazon.com/sns/ SQS: https://aws.amazon.com/sqs/ https://aws.amazon.com/sqs/ Lambda: https://aws.amazon.com/lambda/ https://aws.amazon.com/lambda/ SageMaker: https://aws.amazon.com/sagemaker/ https://aws.amazon.com/sagemaker/ Other: https://medium.com/aws-lambda-serverless-developer-guide-with-hands/dynamodb-streams-using-aws-lambda-to-process-dynamodb-streams-for-change-data-capture-2e3ab8df27ca
https://aws.amazon.com/event-driven-architecture/ https://medium.com/aws-lambda-serverless-developer-guide-with-hands/dynamodb-streams-using-aws-lambda-to-process-dynamodb-streams-for-change-data-capture-2e3ab8df27ca https://medium.com/aws-lambda-serverless-developer-guide-with-hands/dynamodb-streams-using-aws-lambda-to-process-dynamodb-streams-for-change-data-capture-2e3ab8df27ca https://aws.amazon.com/event-driven-architecture/ https://aws.amazon.com/event-driven-architecture/

Inside the Quietly Powerful Architecture That Could Power User Recommendations

According to CoinMarketCap (CMC), there are over 10,000 coins and tokens available to trade in different markets worldwide. Among them, we can find all kinds of things, from old-established cryptocurrencies like Bitcoin and Obyte to experimental DeFi platforms and memecoins. However, you might not know that, in addition to a vast array of tokens, there’s also a diverse selection of free crypto-related tools —ranging from practical to quirky and entertaining. CMC and CoinGecko are, for instance, two quite popular and useful crypto tools, but they’re far from being the only ones. We can discover some interesting things if we just look around a bit. Let’s see some platforms in this category. When Lambo? Memecoins are quite the thing now, but the crypto community always had its own internal memes and jokes without related tokens. “When Lambo” is an old one and a very popular one too. It implies a momentous question: when will we be able to afford an expensive Lamborghini car with our current crypto holdings? It’s known that prices change rapidly in the crypto space, so can you maybe get richer tomorrow with the same number of coins? We can dream, at least. “When Lambo” is so widespread around here that some years ago CMC changed all the prices from USD to LAMBOs in April’s Fools. And the question has its own calculator, to help you find out once and for all how much of a Lambo you can buy with your current holdings in different coins. The site Crypto to Lambo allows you to type the amount, choose between 100 coins and three Lamborghini models, and calculate, well, when Lambo? CMC changed CMC changed The site Crypto to Lambo allows you to type the amount, choose between 100 coins and three Lamborghini models, and calculate, well, when Lambo? Crypto to Lambo Crypto to Lambo The results will include what Lambo you can buy with your coins (at least a toy Lambo?), how many more tokens you need to buy it, the needed price per coin you’ll need to afford it, and the current percentage you have in your wallet. It’ll also lead you to trade on the exchanges Binance or Bitvavo, but that’s an additional option. Do your own research first! Wallet Scrutiny Cryptocurrencies are mostly decentralized, which means you have full control over your holdings if you have a private key. But wait a second, because there might be a trick there. The coins and their ledger are decentralized, yes, but the wallet you use to interact with crypto networks can be a completely different story. Wallets are often offered by middlemen (companies and developer teams), who generally have good intentions —but that’s not always the case. Some awful things (backdoors and other traps) could be hidden in their codes, beyond what we can see. Even if the wallets are open-source software, which means anyone can check their codes, not everyone knows how to. And worse: cybercriminals are designing fake wallets with the same name and branding as legitimate ones, aiming to trick users into downloading this malicious software from official app stores to steal their private keys. Some awful things (backdoors and other traps) could be hidden in their codes, beyond what we can see. Even if the wallets are open-source software, which means anyone can check their codes, not everyone knows how to Now, of course, there’s a way to avoid these bad scenarios, and we have several easy and free tools available for it. WalletScrunity is one of them, focused primarily on Bitcoin wallets, but basic information about others is available too. On this site, anyone can just type the name of their preferred wallet (software or hardware) and discover if it passed the tests and questions from their developers. Among those tests, they verify who’s the team behind the brand, if it’s open-source or not, if it’s non-custodial (offer private keys) or not, and if it’s the original product. They also offer safe links to download or buy the correct version. WalletScrunity WalletScrunity Crypto Bubbles With all their detailed data and long lists, sites to check cryptocurrency market caps, like CMC and CoinGecko, can be a tad overwhelming for beginners. Crypto Bubbles offers a more visual, dynamic experience to check the same data, using colorful bubbles and user-friendly interfaces to do it. The first thing you’ll see on this site is a lot of crypto logos and tickers floating around in bubbles of different sizes and colors, indeed. Crypto Bubbles offers a more visual, dynamic experience to check the same data, using colorful bubbles and user-friendly interfaces to do it. Crypto Bubbles Crypto Bubbles Green bubbles mean price increases —the larger the bubble, the higher the percentage change or market cap. Red bubbles imply negative percentages, and they also change in size, depending on the figures. It’s possible to order them per period (hour, day, week, month, year) or market cap size. In the Settings, they’ll let you change the currency for the prices (USD, EUR, CAD, etc.), languages, colors, and watchlists. If you click a bubble, more specific data, charts, and links about that currency will appear, summed up in a very graphical manner. Below the bubbles, a more traditional list of coins is also available to order by market cap, price, volume, or time changes. Or you can use their search bar to find your favorite coins. An app for iOS and Android can be downloaded too. TxCity Network explorers aren’t exactly the most colorful and user-friendly thing in the world. And let’s not talk about those long, incomprehensible alphanumeric strings that represent transactions and addresses. TxCity was created to see crypto transactions in a new, clearer light. By representing each transaction as a person attempting to board a bus—where buses symbolize blocks in a blockchain— this platform offers users a unique, real-time perspective on the available crypto networks. By representing each transaction as a person attempting to board a bus—where buses symbolize blocks in a blockchain— this platform offers users a unique, real-time perspective on the available crypto networks. Obviously, those networks are only blockchains — they are the kind of networks that have buses and bus drivers. This approach demystifies on-chain data, effectively converting mere transactional networks into visual ecosystems. https://x.com/tastycrypto/status/1887589349677744200?embedable=true https://x.com/tastycrypto/status/1887589349677744200?embedable=true By using TxCity, anyone can visualize transactions across multiple networks, including Bitcoin, Ethereum, Bitcoin Cash, Monero, and Litecoin. The platform also plans to introduce features like bridge visualization between Ethereum and LUKSO, providing users with a real-time experience of their cross-chain transactions. Additionally, TxCity aims to represent wallets as characters or avatars, simplifying user interactions. TxCity TxCity To begin using TxCity, simply visit their website and select the chain you wish to explore. The platform will display live transactions as animated characters attempting to board buses, offering an engaging way to observe how transactions are processed and included in blocks. This visual representation not only makes it easier to understand crypto operations but also serves as an educational tool for those new to this space. Order a Pizza in Obyte Did you know you can order a Hawaiian, Pepperoni, or Mexican pizza using an Obyte chatbot? Well, at least as a role-play! The Obyte Testnet Wallet offers a fun and risk-free way to explore cryptocurrency transactions, including interacting with chatbots. By following simple steps, users can simulate making a purchase and experience how payments work within the Obyte ecosystem. It’s an engaging way to see how digital payments function while learning about Obyte’s capabilities in a relaxed and playful environment. The Obyte Testnet Wallet offers a fun and risk-free way to explore cryptocurrency transactions, including interacting with chatbots. Obyte Testnet Wallet Obyte Testnet Wallet A testnet is a duplicate version of a crypto network designed for learning, development, and testing. Unlike the mainnet, where transactions involve real assets, the testnet allows users to practice without any financial risk. The Obyte Testnet Wallet is a safe space where developers can refine their applications and everyday users can experiment with different features. To start, users can claim free testnet coins—tokens without market value—to send transactions, explore smart contracts, and even test advanced security measures like two-factor authentication (2FA) or multi-signature accounts. A testnet A testnet free testnet coins free testnet coins Beyond simulated pizza purchases, the testnet wallet offers a variety of features to explore. Users can try chatbots from the Bot Store, set up conditional payments, and experiment with wallet settings. It’s also possible to add new devices and test secure account recovery options. Everything is recorded on the testnet explorer, providing insights into how transactions are processed. Since no real money is involved, users can experiment freely and gain confidence before switching to the actual Obyte wallet. testnet explorer testnet explorer Obyte wallet Obyte wallet Featured Vector Image by Freepik Featured Vector Image by Freepik Freepik Freepik

5 Quirky and Useful Crypto Tools You Didn’t Know Before

I never thought I'd write one of those "this tool changed everything" posts. They usually feel... overblown? But here I am, because the shift in my work has been real enough that colleagues have started asking what I'm doing differently. It wasn't some dramatic moment. No lightbulb. Just a gradual realization that my relationship with information had fundamentally changed after adding Claude Desktop with MCP to my workflow. Like most PMs, I'd gotten used to the constant whiplash of switching between tools all day. Research tabs everywhere. Docs scattered across drives. Endless Slack threads. Analytics dashboards. It was just "how things are," right? The tax we pay for working at the intersection of users, business, and tech. What I didn't fully appreciate until Claude Desktop was just how much mental bandwidth this fragmentation was costing me. The constant refresh of context. The nagging worry about insights falling through cracks. The energy spent managing tools instead of solving problems. The tools that made the difference If you're in product, you'll recognize the feeling - we're synthesis machines trapped in fragmented digital workspaces. What changed things for me was having these MCP capabilities all working together: The search tools (tavily-search and fetch) eliminated my tab chaos. Instead of 30+ browser windows, I could pull exactly what I needed and trust it would be there when needed again. The file tools (directory_tree and search_files) gave me a structured way to organize product knowledge without having to remember where everything lived. Web automation with puppeteer tools meant I could stop doing repetitive competitor checks manually. But honestly? The sequentialthinking tool was the game-changer. Having a space to work through complex decisions step by step, with the ability to revise thinking and branch into different scenarios - that's when I realized this wasn't just another productivity tool. Real stuff I've done (still kinda amazed) Two recent projects showed me just how different this approach is: I had to analyze a competitor's enterprise search product. Not just feature-comparison stuff, but truly understanding what was happening under the hood. Using Claude's research capabilities, I built a shockingly detailed 45-page analysis that uncovered the specific signals and parameters they were likely using in their algorithm. It pulled from tons of sources across the internet, found patterns I might have missed, and synthesized it all into something coherent. Another time, a friend was in a desperate time crunch for a marketing campaign. Everything from strategy to all the campaign assets needed to be created ASAP. I helped by having Claude develop a complete marketing plan with all the artifacts - LinkedIn posts, email sequences, landing page content, even conference booth materials. We did in 3 HOURS what would have taken weeks otherwise. My friend was floored. The human stuff - what really surprised me The productivity gains were expected (and welcome!). What I didn't anticipate was how this would change my thinking quality. When your brain isn't constantly task-switching and managing information overload, you start noticing things. I've found myself having deeper empathy for users because I'm actually present in conversations instead of frantically taking notes. I'm seeing connections between feedback that previously would have lived in separate tools and mental spaces. And my decision quality? It's just better. Not because the AI is making decisions (it's not), but because I have the mental space to consider more angles and explore implications more fully. Some days I catch myself getting to the end of a complex work session and realizing I haven't once felt that scattered, overwhelmed feeling that used to be my constant companion. That's when it hit me how much energy I'd been spending just managing information rather than thinking about it. This isn't about AI replacing product thinking. It's about removing the friction that has been getting in the way of our best product thinking. The context switching. The tool juggling. The constant fear of missing important connections. I still have so much to learn about integrating these tools into my workflow. But I'm never going back to the fragmented digital workspace I accepted for years. The mental clarity is too valuable. Curious—has anyone else found a tool or approach that changed the way you think, not just how fast you work? Drop your story below—I’d love to compare notes.

Claude Desktop + MCP Quietly Transformed my Product Thinking

Handling JSON, Schema Issues, and Doris Quirks in an Apache SeaTunnel Pipeline

How To Introduce a New API Quickly Using Spring Boot and Maven

Authors:
(1) Soyeong Jeong, School of Computing;
(2) Jinheon Baek, Graduate School of AI;
(3) Sukmin Cho, School of Computing;
(4) Sung Ju Hwang, Korea Advanced Institute of Science and Technology;
(5) Jong C. Park, School of Computing. Authors: Authors: (1) Soyeong Jeong, School of Computing; (2) Jinheon Baek, Graduate School of AI; (3) Sukmin Cho, School of Computing; (4) Sung Ju Hwang, Korea Advanced Institute of Science and Technology; (5) Jong C. Park, School of Computing. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related Work 2 Related Work 3 Method and 3.1 Preliminaries 3 Method and 3.1 Preliminaries 3.2 Adaptive-RAG: Adaptive Retrieval-Augmented Generation 3.2 Adaptive-RAG: Adaptive Retrieval-Augmented Generation 4 Experimental Setups and 4.1 Datasets 4 Experimental Setups and 4.1 Datasets 4.2 Models and 4.3 Evaluation Metrics 4.2 Models and 4.3 Evaluation Metrics 4.4 Implementation Details 4.4 Implementation Details 5 Experimental Results and Analyses 5 Experimental Results and Analyses 6 Conclusion, Limitations, Ethics Statement, Acknowledgements, and References 6 Conclusion, Limitations, Ethics Statement, Acknowledgements, and References A Additional Experimental Setups A Additional Experimental Setups B Additional Experimental Results B Additional Experimental Results 3.2 Adaptive-RAG: Adaptive Retrieval-Augmented Generation We now introduce our adaptive retrieval-augmented LLMs, which are built upon three different strategies described in the previous section, and which are designed to select the most suitable strategy according to the complexity of queries. Adapting Retrieval-Augmented LLMs Note that in real-world scenarios, not all q from users have the same level of complexity, necessitating tailored strategies for handling each query. In other words, employing the most basic, non-retrieval based approach LLM(q) to respond to the complex query q would be also ineffective (Figure 2, A); conversely, using a more elaborate multi-step approach LLM(q, d, c) for simple q would be inefficient (Figure 2, B). Therefore, our adaptive framework is designed to dynamically adjust the query handling strategy of retrieval-augmented LLMs, which is achieved by determining the complexity of each query before attempting a solution. Notably, this framework can offer a robust middle ground with a range of solutions, from the simplest approach for the most straightforward queries, to the one-step approach for moderate queries, and up to the most comprehensive and rigorous approach for complex queries. In addition, since the operations of LLM and Retriever remain consistent regardless of inputs to them, our method can seeminglessly go back and forth across queries of different complexities, without changing the internal model architecture or parameters during adaption. Adapting Retrieval-Augmented LLMs Query Complexity Assessment To operationalize our adaptive retrieval-augmented LLM framework, we should determine the query complexity, and to achieve this, we propose to model a complexity classifier, whose goal is to return the appropriate complexity level of the given query. Specifically, given the query q, our classifier can be formulated as follows: o = Classifier(q), where Classifier is a smaller Language Model that is trained to classify one of three different complexity levels and o is its corresponding class label. In our classifier design, there are three class labels: ‘A’, ‘B’, and ‘C’, where ‘A’ indicates that q is straightforward and answerable by LLM(q) itself, ‘B’ indicates that q has the moderate complexity where at least a single-step approach LLM(q, d) is needed, and ‘C’ indicates that q is complex, requiring the most extensive solution LLM(q, d, c) [2]. Training Strategy The remaining step is to train the smaller Language Model for Classifier, to accurately predict its complexity o in response to the given query q. Yet, there is no annotated dataset available for query-complexity pairs. Hence, we propose to automatically construct the training dataset with two particular strategies. Training Strategy To be specific, we first aim at labeling the query complexity based on the results from three different retrieval-augmented LLM strategies, in order to determine the label by its needs. For example, if the simplest non-retrieval-based approach correctly generates the answer, the label for its corresponding query is assigned ‘A’. Also, to break the tie between different models in providing the label to the query, we provide a higher priority to a simpler model. In other words, if both single-step and multi-step approaches produce the same correct answer while the non-retrieval-based approach fails, we assign label ‘B’ to its corresponding query. However, this labeling strategy has a limitation in that not all the queries are assigned labels, since the three retrieval-augmented approaches may all fail to generate the correct answer. On the other hand, the benchmark datasets may already have meaningful inductive biases about the most appropriate retrieval-augmented LLM strategies for their queries, considering the ways they are created (e.g., QA datasets that require sequential reasoning usually necessitate a multi-step approach; while queries of those with labeled single documents can be ideally answerable with the single-step approach). Therefore, for those queries that remain unlabeled after the first labeling step, we assign ‘B’ to queries in single-hop datasets and ‘C’ to queries in multi-hop datasets. Finally, we train Classifier with these automatically collected query-complexity pairs[3], by using a crossentropy loss. Then, at inference, we can determine the complexity of the query, which is one of {‘A’, ‘B’, ‘C’}, by forwarding it to Classifier: o = Classifier(q). 4 Experimental Setups In this section, we explain datasets, models, metrics, and implementation details. We provide additional details in Appendix A. 4.1 Datasets In order to simulate a realistic scenario, where different queries have varying complexities, we use both the single-hop and multi-hop QA datasets simultaneously, in the unified experimental setting. Single-hop QA For simpler queries, we use three benchmark single-hop QA datasets, which consist of queries and their associated documents containing answers, namely 1) SQuAD v1.1 (Rajpurkar et al., 2016), 2) Natural Questions (Kwiatkowski et al., 2019), and 3) TriviaQA (Joshi et al., 2017). Single-hop QA 1) SQuAD v1.1 2) Natural Questions 3) TriviaQA Multi-hop QA To consider more complex query scenarios, we use three benchmark multi-hop QA datasets, which require sequential reasoning over multiple documents, namely 1) MuSiQue (Trivedi et al., 2022a), 2) HotpotQA (Yang et al., 2018), and 3) 2WikiMultiHopQA (Ho et al., 2020). 1) MuSiQue 2) HotpotQA 3) 2WikiMultiHopQA This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv [2] We consider three levels of query complexity, and leave the exploration of more fine-grained complexities as future work.

Query Quirks: AI’s Witty Way of Sorting Brain Teasers

How To Introduce a New API Quickly Using Spring Boot and Gradle

Jak rychle zavést nové API pomocí Spring Boot a Gradle

Sådan introduceres en ny API hurtigt ved hjælp af Spring Boot og Gradle

Cómo introducir una nueva API rápidamente usando Spring Boot y Gradle

Como introducir unha nova API rapidamente usando Spring Boot e Gradle

Kako brzo predstaviti novi API koristeći Spring Boot i Gradle

Új API gyors bevezetése a Spring Boot és a Gradle használatával

כיצד להציג ממשק API חדש במהירות באמצעות Spring Boot ו-Gradle

Spring Boot と Gradle を使用して新しい API を迅速に導入する方法

Sida Loo Soo Bandhigo API Cusub Si Dhakhso leh Isticmaalka Bootka Guga iyo Gradle

Як швидко представити новий API за допомогою Spring Boot і Gradle

Spring Boot va Gradle-dan foydalanib, yangi API-ni qanday tezda joriy qilish mumkin

如何使用 Spring Boot 和 Gradle 快速引入新 API

The Big Picture: Shift to Action-Based AI You should have already felt that AI systems are shifting from just being passive tools to active “agentic“ solutions. The past year saw a “real acceleration” in AI progress, and experts predict it will “move beyond just generating text and images and morph into agents that can complete complex tasks”. This leap is exciting, but some warn it could also “diminish human control” if left unchecked. In short, AI is no longer just answering questions – it’s poised to take action in the real world. AI systems do not just chat anymore; they act. Instead of merely suggesting an action, an advanced AI system can execute the action itself. ChatGPT’s latest features exemplify this shift: it “enables AI systems to retrieve data, process information, and take action” using tools like web browsers and databases. In other words, an AI agent can carry out multi-step jobs end-to-end. These agents are now seen as an “active digital workforce” augmenting human teams. A support bot might not only draft a reply but also send it and update records on its own, and not just through API but through a web interface. 🫣 CUA and OpenAI’s Responses API: Building Agentic AI To develop action-oriented AI systems, many organizations are rolling out new frameworks. OpenAI, for instance, just introduced tools for agentic applications, including a Responses API that pairs GPT-style language skills with built-in web search, file handling, and a computer-using agent (CUA) to operate the software. In practice, an AI using these tools could fill out forms or update databases by itself. OpenAI also released an Agents SDK to orchestrate such workflows and made it open-source. Similarly, open-source projects like open-cuak and browser-use provide “Operator”-style automation without proprietary fees. These efforts lower the barrier to creating AI agents that can not only think but also act on a user’s behalf. Large Action Models (LAMs): Beyond Language to Execution An LAM is essentially a large AI model (basically a fined-tuned multi-modal LLM) that doesn’t just understand language – it can do things in response. Think of it as the “can-do cousin” of a large language model. Where a traditional assistant might tell you how to solve a problem, a LAM-powered system could actually carry out the solution for you. This represents a major shift, potentially allowing AI to automate entire processes rather than just generate content. LAMs do this by combining language understanding with decision-making and tool use. By turning AI from a passive responder into an active problem-solver, they unlock new possibilities – but also demand greater reliability, since mistakes by an agent that acts can have real consequences. The Dead Internet Theory and AI Saturation One side effect of these agentic advances is a flood of AI-generated content online. This has revived the Dead Internet Theory, which claims much of today’s online content is produced by bots rather than people. In 2025, that idea feels eerily plausible. A significant portion of tweets, posts, and even news articles are now machine-generated, blurring the line between genuine human voices and automated output. AI bots even amplify each other’s content, creating fake engagement cycles with little human involvement. As one tech journalist asked, “with a large portion of the internet being AI-generated content, is a ‘dead internet’ really that far off?” This saturation of AI content makes it harder to know what’s real online. We may need new tools to verify human-made material and keep the internet feeling “alive” with authentic interaction. Conclusion: Balancing Potential With Oversight The evolution of AI into autonomous agents brings great promise – and calls for prudence. On one hand, these agents can handle drudge work and complex tasks at lightning speed, freeing people to focus on creativity and strategy. On the other hand, giving AI the freedom to act means setting clear guardrails. As some experts warn, AI’s impact could “either save humanity or [gradually] destroy it” depending on how responsibly we guide it. That may sound extreme, but it underscores the importance of oversight. Accordingly, developers and policymakers are crafting safety guidelines to keep agentic AI aligned with human values. Keeping a human in the loop for critical decisions remains vital. Ultimately, it’s about balance: harnessing AI’s potential to act on our behalf, while ensuring humans retain final control. If we get it right, autonomous agents could become invaluable partners – powerful, yet under human direction. Feature Image is generated with Google's ImageFX The Big Picture: Shift to Action-Based AI The Big Picture: Shift to Action-Based AI You should have already felt that AI systems are shifting from just being passive tools to active “agentic“ solutions. The past year saw a “real acceleration” in AI progress, and experts predict it will “move beyond just generating text and images and morph into agents that can complete complex tasks” . This leap is exciting, but some warn it could also “diminish human control” if left unchecked. In short, AI is no longer just answering questions – it’s poised to take action in the real world. “agentic“ “real acceleration” “move beyond just generating text and images and morph into agents that can complete complex tasks” “move beyond just generating text and images and morph into agents that can complete complex tasks” “diminish human control” “diminish human control” take action AI systems do not just chat anymore; they act. Instead of merely suggesting an action, an advanced AI system can execute the action itself. ChatGPT’s latest features exemplify this shift: it “enables AI systems to retrieve data, process information, and take action” using tools like web browsers and databases. In other words, an AI agent can carry out multi-step jobs end-to-end. These agents are now seen as an “active digital workforce” augmenting human teams. A support bot might not only draft a reply but also send it and update records on its own, and not just through API but through a web interface. 🫣 “enables AI systems to retrieve data, process information, and take action” “enables AI systems to retrieve data, process information, and take action” “active digital workforce” “active digital workforce” CUA and OpenAI’s Responses API: Building Agentic AI CUA and OpenAI’s Responses API: Building Agentic AI To develop action-oriented AI systems, many organizations are rolling out new frameworks. OpenAI, for instance, just introduced tools for agentic applications, including a Responses API that pairs GPT-style language skills with built-in web search, file handling, and a computer-using agent (CUA) to operate the software. In practice, an AI using these tools could fill out forms or update databases by itself. OpenAI also released an Agents SDK to orchestrate such workflows and made it open-source. Similarly, open-source projects like open-cuak and browser-use provide “Operator” -style automation without proprietary fees. These efforts lower the barrier to creating AI agents that can not only think but also act on a user’s behalf. agentic computer-using agent Agents SDK open-cuak browser-use “Operator” Large Action Models (LAMs): Beyond Language to Execution Large Action Models (LAMs): Beyond Language to Execution An LAM is essentially a large AI model (basically a fined-tuned multi-modal LLM) that doesn’t just understand language – it can do things in response. Think of it as the “can-do cousin” of a large language model. Where a traditional assistant might tell you how to solve a problem, a LAM-powered system could actually carry out the solution for you. This represents a major shift, potentially allowing AI to automate entire processes rather than just generate content. LAMs do this by combining language understanding with decision-making and tool use. do “can-do cousin” “can-do cousin” By turning AI from a passive responder into an active problem-solver, they unlock new possibilities – but also demand greater reliability, since mistakes by an agent that acts can have real consequences. The Dead Internet Theory and AI Saturation The Dead Internet Theory and AI Saturation One side effect of these agentic advances is a flood of AI-generated content online. This has revived the Dead Internet Theory , which claims much of today’s online content is produced by bots rather than people. In 2025, that idea feels eerily plausible. A significant portion of tweets, posts, and even news articles are now machine-generated, blurring the line between genuine human voices and automated output. Dead Internet Theory AI bots even amplify each other’s content, creating fake engagement cycles with little human involvement. As one tech journalist asked, “with a large portion of the internet being AI-generated content, is a ‘dead internet’ really that far off?” This saturation of AI content makes it harder to know what’s real online. We may need new tools to verify human-made material and keep the internet feeling “alive” with authentic interaction. “with a large portion of the internet being AI-generated content, is a ‘dead internet’ really that far off?” “with a large portion of the internet being AI-generated content, is a ‘dead internet’ really that far off?” Conclusion: Balancing Potential With Oversight Conclusion: Balancing Potential With Oversight The evolution of AI into autonomous agents brings great promise – and calls for prudence. On one hand, these agents can handle drudge work and complex tasks at lightning speed, freeing people to focus on creativity and strategy. On the other hand, giving AI the freedom to act means setting clear guardrails . guardrails As some experts warn, AI’s impact could “either save humanity or [gradually] destroy it” depending on how responsibly we guide it. That may sound extreme, but it underscores the importance of oversight. Accordingly, developers and policymakers are crafting safety guidelines to keep agentic AI aligned with human values. “either save humanity or [gradually] destroy it” “either save humanity or [gradually] destroy it” Keeping a human in the loop for critical decisions remains vital. Ultimately, it’s about balance: harnessing AI’s potential to act on our behalf, while ensuring humans retain final control. If we get it right, autonomous agents could become invaluable partners – powerful, yet under human direction . human in the loop under human direction Feature Image is generated with Google's ImageFX Feature Image is generated with Google's ImageFX

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