2025-03-10 03:00:03
Talk to anyone in AI, analytics, or data science, and they’ll tell you synthetic data is the future. But ask them what they mean by “synthetic data,” and you’ll get wildly different answers. That’s because synthetic data isn’t just one thing—it’s a broad category with multiple use cases and definitions. And that ambiguity makes conversations confusing.
\ So, let’s cut through the noise. At its core, synthetic data operates along two key dimensions. The first is a spectrum ranging from filling in missing data in an existing dataset to generating entirely new datasets. The second distinguishes between interventions at the raw data level versus interventions at the insights or outcomes level.
\ Imagine these dimensions as axes on a chart. This creates four quadrants, each representing a different type of synthetic data: data imputation, user creation, insights modeling, and manufactured outcomes. Each one serves a distinct function, and if you’re working with data in any capacity, you need to know the difference.
\
While some might argue that data imputation isn’t truly synthetic data, modern imputation techniques have evolved beyond simple mean or median substitution. Today, advanced imputation leverages machine learning and generative AI models, making the generated values more sophisticated and contextually relevant than ever before.
\ Data imputation sits at the intersection of missing data and raw data intervention. This means we’re working with existing datasets that have gaps, and our goal is to generate plausible values to complete them. Unlike other types of synthetic data, imputation isn’t about creating entirely new information—it’s about making incomplete data more usable.
\ Example: A market research firm conducting media effectiveness studies might have gaps in its audience response data due to missing survey responses. Instead of discarding incomplete datasets, imputation techniques—such as statistical modeling or machine learning—can generate realistic estimates, ensuring analysts can still draw meaningful insights from the data.
User creation lies between new data generation and raw data intervention. Instead of modifying existing data, this approach fabricates entirely new user profiles and behaviors. It’s particularly useful when real user data isn’t available, is sensitive, or needs to be scaled artificially.
\ User creation is a game-changer for testing products, improving security, and training AI models.
\ Example: A streaming service might create synthetic user profiles to test its recommendation engine without exposing real customer data. Cybersecurity firms do the same to simulate attack scenarios and train fraud detection systems.
Insights modeling operates at the intersection of existing data and intervention at the insights level. Instead of manipulating raw data points, it creates datasets that preserve the statistical properties of real-world data without exposing actual records. This makes it ideal for privacy-sensitive applications.
\ Insights modeling also allows researchers to scale insights from pre-existing datasets, particularly when gathering large-scale data is impractical. This is common in marketing research, where data collection can be cumbersome and costly. However, this approach requires a solid foundation of real-world training data.
\ Example: A market research firm conducting copy testing might use insights modeling to scale its normative database. Instead of relying solely on collected survey responses, the firm can generate synthetic insights models that extrapolate patterns from existing normative data. This allows brands to test creative performance against a broader, more predictive dataset without continuously gathering new survey responses.
Manufactured Outcomes sit at the extreme end of both new data generation and insights-level intervention. This approach involves generating entirely new datasets from scratch to simulate environments or scenarios that don’t yet exist but are essential for AI training, modeling, and simulations.
\ Sometimes, the data you need simply doesn’t exist—or is too expensive or dangerous to collect in the real world. That’s where Manufactured Outcomes come in. This process generates entirely new datasets, often to train AI systems in environments that are difficult to replicate.
\ Example: Self-driving car companies generate synthetic road scenarios—like a pedestrian suddenly jaywalking—to train their AI on rare but critical situations that might not appear often in real-world driving footage.
While synthetic data provides powerful solutions, it isn’t without risks. Each type of synthetic data has its own challenges that can impact data quality, reliability, and ethical use. Here are some key concerns to keep in mind:
\
To ensure synthetic data meets quality standards, consider these questions:
\
Synthetic data is a broad term, and if you’re working in AI, analytics, or any data-driven field, you need to be clear on what kind you’re dealing with. Are you filling in missing data (imputation), creating test users (user creation), generating anonymized patterns (insights modeling), or building brand-new datasets from scratch (manufactured outcomes)?
\ Each of these plays a different role in how we use and protect data, and understanding them is key to making informed decisions in the rapidly evolving world of AI and data science. So next time someone throws around the term “synthetic data,” ask them: Which kind?
2025-03-10 02:00:03
Imagine asking a robot: "Hey, pick up the red cup from the kitchen and bring it here."
\ Sounds simple right? But for AI this involves understanding language, navigating a space, recognizing objects, and providing feedback all in real time.
\ This is exactly what I tackled in the Alexa Prize SimBot Challenge where we built an embodied conversational agent that could understand instructions, move through its environment, interact with objects, and communicate back.
\ Here’s how we made it work using BERT, reinforcement learning, and multimodal machine learning. Let’s go through the different problems and how we tackled each of them.
Natural language is messy and can get very complicated. We humans say Go to the fridge but could also say Find the fridge and open it. A robot must extract meaning from different phrasings.
\ To do this, we used BERT (Bidirectional Encoder Representations from Transformers) to convert text instructions into structured commands, so that it’s easier for it to execute them in a sequential manner.
\ How It Works
\ Below is the core of our BERT-based instruction parser:
import torch
import torch.nn as nn
import torch.optim as optim
from transformers import BertTokenizer, BertModel
class InstructionEncoder(nn.Module):
"""
Fine-tunes BERT on domain-specific instructions, outputs a command distribution.
"""
def __init__(self, num_commands=10, dropout=0.1):
super(InstructionEncoder, self).__init__()
self.bert = BertModel.from_pretrained("bert-base-uncased")
self.dropout = nn.Dropout(dropout)
self.classifier = nn.Linear(self.bert.config.hidden_size, num_commands)
def forward(self, input_ids, attention_mask):
outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask)
pooled_output = outputs.pooler_output
pooled_output = self.dropout(pooled_output)
logits = self.classifier(pooled_output)
return logits
#Suppose we have some labeled data: (text -> command_id)
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
model = InstructionEncoder(num_commands=12)
model.train()
instructions = ["Go to the fridge", "Pick up the red cup", "Turn left"]
labels = [2, 5, 1]
input_encodings = tokenizer(instructions, padding=True, truncation=True, return_tensors="pt")
labels_tensor = torch.tensor(labels)
optimizer = optim.AdamW(model.parameters(), lr=1e-5)
criterion = nn.CrossEntropyLoss()
\
Once the robot understands where to go it needs a way to get there. We used A* search for structured environments (like maps) and Reinforcement Learning (RL) for dynamic spaces.
\ This is how we implemented our A* search implementation for pathfinding.
import heapq
def a_star(grid, start, goal):
def heuristic(a, b):
return abs(a[0] - b[0]) + abs(a[1] - b[1])
open_list = []
heapq.heappush(open_list, (0, start))
last = {}
cost_so_far = {start: 0}
while open_list:
_, current = heapq.heappop(open_list)
if current == goal:
break
for dx, dy in [(-1, 0), (1, 0), (0, -1), (0, 1)]: #4 directions
neighbor = (current[0] + dx, current[1] + dy)
if neighbor in grid: #Check if it's a valid position
new_cost = cost_so_far[current] + 1
if neighbor not in cost_so_far or new_cost < cost_so_far[neighbor]:
cost_so_far[neighbor] = new_cost
priority = new_cost + heuristic(goal, neighbor)
heapq.heappush(open_list, (priority, neighbor))
last[neighbor] = current
return last
\ And this is the implementation of how we use RL for dynamic movement.
\
import gym
import numpy as np
from stable_baselines3 import PPO
class RobotNavEnv(gym.Env):
"""
A simplified environment mixing a partial grid with dynamic obstacles.
Observations might include LiDAR scans or collision sensors.
"""
def __init__(self):
super(RobotNavEnv, self).__init__()
self.observation_space = gym.spaces.Box(low=0, high=1, shape=(360,), dtype=np.float32)
self.action_space = gym.spaces.Discrete(3)
self.state = np.zeros((360,), dtype=np.float32)
def reset(self):
self.state = np.random.rand(360).astype(np.float32)
return self.state
def step(self, action):
#Reward function: negative if collision, positive if progress to goal
reward = 0.0
done = False
if action == 2 and np.random.rand() < 0.1:
reward = -5.0
done = True
else:
reward = 1.0
self.state = np.random.rand(360).astype(np.float32)
return self.state, reward, done, {}
env = RobotNavEnv()
model = PPO("MlpPolicy", env, verbose=1).learn(total_timesteps=5000)
\
Once at the destination, the robot must see and interact with objects. This required computer vision for object localization.
\ We trained a YOLOv8 model to recognize objects like cups, doors, and appliances.
\
import torch
from ultralytics import YOLO
import numpy as np
#load a base YOLOv8 model
model = YOLO("yolov8s.pt")
#embeddings
object_categories = {
"cup": np.array([0.22, 0.88, 0.53]),
"mug": np.array([0.21, 0.85, 0.50]),
"bottle": np.array([0.75, 0.10, 0.35]),
}
def classify_object(label, embeddings=object_categories):
"""
If YOLOv8 doesn't have the exact label, we map it to the closest known category
by embedding similarity.
"""
if label in embeddings:
return label
else:
best_label = None
best_sim = -1
for cat, emb in embeddings.items():
sim = np.random.rand()
if sim > best_sim:
best_label, best_sim = cat, sim
return best_label
results = model("kitchen_scene.jpg")
for r in results:
for box, cls_id in zip(r.boxes.xyxy, r.boxes.cls):
label = r.names[int(cls_id)]
mapped_label = classify_object(label)
\
Now that the robot:
\ It needs to understand how to respond to the user. This feedback loop also helps in the user experience; to achieve this, we used a GPT-based text generation for dynamic responses.
\
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-j-6B")
model_gpt = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-j-6B").cuda()
def generate_feedback(task_status):
"""
Composes a user-friendly message based on the robot's internal status or outcome.
"""
prompt = (f"You are a helpful home robot. A user gave you a task. Current status: {task_status}.\n"
f"Please provide a short, friendly response to the user:\n")
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model_gpt.generate(**inputs, max_length=60, do_sample=True, temperature=0.7)
response_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response_text.split("\n")[-1]
print(generate_feedback("I have arrived at the kitchen. I see a red cup."))
\
The synergy of advanced NLP, robust path planning, real-time object detection, and generative language has opened a new frontier in collaborative robotics. Our agents can interpret nuanced commands, navigate dynamic environments, identify objects with remarkable accuracy, and deliver responses that feel natural.
\ Beyond simple task execution, these robots engage in genuine back-and-forth communication asking clarifying questions, explaining actions, and adapting on the fly. It’s a glimpse of a future where machines do more than serve: they collaborate, learn, and converse as true partners in our daily routines.
\
2025-03-10 01:59:55
\ The cryptocurrency landscape continues to evolve rapidly, offering opportunities for both seasoned investors and newcomers alike.
\ Among the most promising blockchain protocols in 2025 are Pi Network (PI) and Coldware (COLD), each pioneering revolutionary models capable of creating crypto millionaires. While Pi Network (PI) leverages its mobile-mining model, Coldware (COLD) stands out with an innovative PayFi ecosystem that redefines decentralized finance (DeFi) and real-world financial integration.
Coldware (COLD), positions itself as a tangible, immediately useful blockchain protocol through its innovative PayFi ecosystem. This approach has rapidly gained investor confidence, demonstrated by its impressive presale raising over $1.4 million. Unlike Pi Network (PI), Coldware (COLD) is laser-focused on delivering real-world applications immediately, providing seamless blockchain-based mobile financial solutions to underserved markets globally.
\ \The Coldware (COLD) PayFi ecosystem integrates decentralized finance (DeFi) tools into everyday mobile transactions, democratizing financial services and opening a pathway to genuine financial inclusion. Investors looking beyond speculative gains increasingly see Coldware (COLD) as a superior alternative to Pi Network (PI), due to its immediate real-world applicability.
Pi Network (PI) has captivated millions with its unique approach to cryptocurrency mining, which utilizes mobile devices instead of energy-intensive mining hardware. As Pi Network (PI) prepares for its much-anticipated Binance listing, currently valued around $1.79, excitement among investors suggests its price could rise sharply, potentially exceeding $10.
\ However, despite this excitement, Pi Network (PI) still faces regulatory uncertainties and volatility risks that could affect its mass adoption. Nevertheless, strategic partnerships, such as the real estate integration by Zito Realty LLC, signify Pi Network’s (PI) growing real-world utility, creating new avenues for investors to gain wealth through practical blockchain adoption.
Both Pi Network (PI) and Coldware (COLD) offer revolutionary blockchain solutions with significant potential for massive returns. Yet, their distinct strategies attract different investor profiles:
\
\
\ While Pi Network (PI) investors are banking on potential future adoption and speculative increases, Coldware (COLD) provides certainty through clear use cases, real financial services, and blockchain-driven financial inclusion from day one.
Analysts agree both Pi Network (PI) and Coldware (COLD) possess the necessary components to generate significant wealth. Pi Network’s potential Binance listing could rapidly drive speculative investment, attracting short-term traders. Coldware’s PayFi technology, on the other hand, helps solving genuine financial pain points, thus having a sustainable, long-term growth trajectory.
\ For more information on the Coldware (COLD) Presale:
\ Visit Coldware (COLD)
\ Join and become a community member:
\ https://x.com/ColdwareNetwork
:::info This article is published under HackerNoon’s Business Blogging program. Do your own research before making any financial decisions.
:::
\
2025-03-10 01:00:02
Together, Bert and Ernie, two friends are working on the side project of their dreams: a chat app that enables real-time conversations for teams who work remotely. The front-end coder Bert can hardly contain himself as he shares shiny new features with his backend specialist friend. “Look! I can type a message, and it gets sent to the server, and I can see a response popping up nearly instantly!” Ernie, the backend guru, frowns. “But how do you solve the issue of two people trying to have a conversation at the same time? Your app keeps refreshing the page instead of letting you ‘see’ the conversation, which makes it impossible to chat in real-time .”
\ The challenge is that Bert’s app operates on HTTP in which a client must request a fetch from the server. Ernie summaries: “It’s like all your friends are texting you back, but then, suddenly, you can’t see any of the replies until you refresh to check if there are new messages because they can’t see your last message until you refresh it and that is how HTTP works!”
\n Here comes the best part: for full-stack apps that work in real-time, there is WebSockets. This part is super exciting, so let’s join Ernie and Bert and find out how WebSockets function, their implementation procedures, the reasoning behind their importance, and so much more.
WebSockets are similar to a continuous phone call between a client (your web browser) and a server. They differ from the HTTP protocol which functions in the manner of, “send a letter, wait for a reply.” With WebSockets, both parties can converse freely as they please. The features of WebSockets include the following:
\
\ Example: Picture a constantly updating sports scoreboard. With HTTP, the client constantly has to spam the server every second, “Any score change?” With WebSockets, the server announces,” GOAL!” the instant it happens.
Bert sits back and scratches his head. “But HTTP works fine for most apps. Why move the goalposts?” Ernie laughs softly, takes a marker, and begins writing something on the whiteboard.
\ “Let’s start from the top,” he says. “Let’s say real-time life updates, such as live sports scores. Clients need to ask the server every second: ‘Is there anything I need to know?’ That is polling, so every thirty seconds, you have to ask, ‘Are we meeting?' Ridiculous, no? It also uses a lot of bandwidth.”
\ WebSockets turn this around:
\ “HTTP is fairly decent at dealing with static things such as a blog or product page. When it comes to live interactions though, a persistent connection is required. WebSockets.”
Ernie cleans his glasses. "So, let’s make a simple chat application,” Ernie exclaims. “We will have a Node.js server for the backend and a React app for the frontend. Ready?”
“Grab a Node.js server because you are going to need it for the Socket.IO part,” Ernie says as he types really quickly:
\
npm init -y
npm install express socket.io
\n Server code (server.js) :
const express = require('express');
const http = require('http');
const { Server } = require('socket.io');
const app = express();
const server = http.createServer(app);
const io = new Server(server);
// Serve static files (like React's build folder)
app.use(express.static('public'));
// Handle WebSocket connections
io.on('connection', (socket) => {
console.log('New user connected:', socket.id);
// Listen for Incoming messages from the socket
socket.on('sendMessage', (message) => {
console.log('Received:', message);
// Broadcast the message to everyone
io.emit('receiveMessage', message);
});
// Handle user disconnects events
socket.on('disconnect', () => {
console.log('User left:', socket.id);
});
});
server.listen(3000, () => {
console.log('Visit http://localhost:3000 on a browser, Server started successful on port 3000');
});
\ “The client and server are able to communicate with each other thanks to Socket.IO’s handling of WebSocket connections. When the client calls the (sendMessage) function, the server sends the message to everyone connected through (io.emit). Pretty straightforward, isn’t it?”
“It’s my turn now. Let’s get to work on the React client,” says Bert.
\
npx create-react-app chat-client
cd chat-client
npm install socket.io-client
\n Chat component (Chat.js) :
import { useState, useEffect } from 'react';
import io from 'socket.io-client';
// Connect to the server
const socket = io('http://localhost:3000');
function Chat() {
const [message, setMessage] = useState('');
const [messages, setMessages] = useState([]);
useEffect(() => {
// Listen for new messages
socket.on('receiveMessage', (newMessage) => {
setMessages([...messages, newMessage]);
});
}, [messages]);
const sendMessage = () => {
if (message.trim()) {
socket.emit('sendMessage', message);
setMessage('');
}
};
return (
<div style={{ padding: '20px' }}>
<div>
{messages.map((msg, index) => (
<p key={index}>{msg}</p>
))}
</div>
<input
value={message}
onChange={(e) => setMessage(e.target.value)}
onKeyPress={(e) => e.key === 'Enter' && sendMessage()}
/>
<button onClick={sendMessage}>Send</button>
</div>
);
}
export default Chat;
\ How It Works :
Test It :
This is what Bert intended to build based on the application, but Ernie mentions, “Most real-life applications need a bit of mistake handling and a way to expand the framework for development.” Let’s see some probable problems.
Implement a reconnection strategy in the React client.
socket.on('disconnect', () => {
console.log('Disconnected. Trying to reconnect...');
socket.connect(); // Auto-reconnect
});
Secure WebSocket using authenticated JWT tokens.
// On login, send a token
socket.emit('authenticate', { token: 'USER_JWT' });
// Server verifies token
socket.on('authenticate', ({ token }) => {
if (isValidToken(token)) {
socket.authenticated = true;
} else {
socket.disconnect();
}
});
Introduce messaging passing between different servers using Redis.
npm install redis
\ Server code :
const redis = require('redis');
const subscriber = redis.createClient();
subscriber.subscribe('chat-channel');
subscriber.on('message', (channel, message) => {
io.emit('receiveMessage', message);
});
Beyond Chat: Editing a shared document
This is Bert’s next idea - an editor similar to Google Docs. Ernie says, “You can use web sockets to listen for changes made by different people and merge them.”
\ For Example :
User A types: “Hello”
User B removes “o” and adds “, World”
The server listens for each keystroke and broadcasts it to all clients, for their respective clients to render an UPDATED document.
\
Code Snippet :
// Client sends keystrokes
textarea.addEventListener('input', (e) => {
socket.emit('textChange', e.target.value);
});
// Server broadcasts changes
socket.on('textChange', (text) => {
io.emit('updateText', text);
});
Ernie warns us that: “You should never use WebSockets without HTTPS/WSS!”
\
const io = new Server(server, { cors: { origin: "https://yourdomain.com" }, });
\
Validate all inputs so as to sanitize any data that could lead to XSS attacks.
\
Implement rate limit on users with spamming behavior.
Q: Is the speed of WebSockets much better than that of HTTP?
A: Yes! For instantaneous applications, WebSockets are the best because they remove the need to conduct polling.
\ Q: Is it possible to utilize REST APIs together with WebSockets?
A: Yes, definitely! Use WebSockets with live updates and REST for performing CRUD operations.
\ Q: How do I deal with a server crashing?
A: Clients can automatically reconnect (see reconnection logic above).
Bert’s chat app now pings with real-time charm and Ernie smiles with satisfaction. WebSockets are not only for chats, but also serve the purpose of live dashboards, gaming, IoT, and more.
\ Your Turn: Go ahead and try out the examples, destroy them, and create something new. Your real-time apps are waiting!
2025-03-10 00:05:03
How are you, hacker?
🪐 What’s happening in tech today, March 9, 2025?
The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, I Invented the Internet in 1999, and we present you with these top quality stories. From Facilitating a Seamless Backend Transition From LevelUp to Paytronix to Heres How to Learn Data Structures the Fun Way With Flutter, let’s dive right in.
By @kitcast [ 7 Min read ] Discover how data visualization transforms raw data into actionable insights for tech companies, driving better decisions and competitive advantage. Read More.
By @marutitechlabs [ 5 Min read ] Maruti Techlabs automated policy data management for HealthPro Insurance, improving efficiency, reducing manual effort, and enhancing data accuracy. Read More.
By @hayday [ 6 Min read ] It Takes More than Thinking: Humans Put the Vibe into Vibe Coding. An article about software engineers in a post-AI world, Vibeware and embracing ourselves Read More.
By @hayday [ 4 Min read ] Is the rise of vibe coding also the end of software engineering? How will vibeware change the nature of the software entrepreneur, and the meaning of work? Read More.
By @dhruvam [ 23 Min read ] Inspired by Google’s Applied CS with Android, this adaptation for Flutter provides an interactive way to understand Arrays, HashSets, and HashMaps. Read More.
By @marutitechlabs [ 5 Min read ] Discover how Maruti Techlabs ensured a seamless transition for Ann’s Boba Tea from LevelUp to Paytronix, preventing downtime and enhancing user experience. Read More.
By @techietales [ 4 Min read ] The upcoming launch of $SESH represents a significant milestone for Session, as it seeks to create a sustainable and decentralized messaging ecosystem. Read More.
By @linked_do [ 11 Min read ] The clock for AI Literacy is ticking. Why should you act now, what are the six pillars of AI Literacy, and how can you build on those? Read More.
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2025-03-10 00:00:07
One-on-one meetings are fundamental to building a high-performance team that can scale to meet business needs and contribute to an organization’s growth.
\ Most managers understand the importance of these meetings, and yet, they either deprioritize them or run them ad hoc without proper planning or paying attention to their effectiveness.
\ They treat them like an item on the to-do list, which feels good once ticked without adding any value to the team’s learning and growth.
\ Not having these discussions is definitely bad, but it’s even worse when these meetings leave people feeling confused, overwhelmed, or agitated because their managers are either unprepared or don’t know how to run them well.
\
Meetings are at the heart of an effective organization, and each meeting is an opportunity to clarify issues, set new directions, sharpen focus, create alignment, and move objectives forward.
— Paul Axtell
\ Use these 5 practices to run effective one-on-one meetings:
Many managers set strict professional boundaries and don’t make an attempt to know their direct reports beyond work.
\ They talk about work all the time—requirements, deadlines, and expectations. To them, nothing else matters. Engaging in small talk or discussing anything that doesn’t involve work seems like a waste of time because they don’t understand the value of building a personal connection and getting a peek into their team’s life outside of work.
\ Your reports are real human beings with emotions, desires, and dreams. They have a life beyond work. Treating them as humans and not resources builds the connection that’s necessary to utilize their full potential.
\ To make your one-on-one meetings highly effective, start with getting to know your team:
\
Strive for all your one-on-one meetings to feel a little awkward. Why? Because the most important and meaningful conversations have that characteristic. It isn’t easy to discuss mistakes, confront tensions, or talk about deep fears or secret hopes, but no strong relationship can be built on superficial pleasantries alone.
— Julie Zhuo
\ Asking these questions without getting too personal can give you just the information needed to communicate effectively and work better together. Knowing them can enable you to adapt the conversation instead of simply following a script.
\ One-on-one meetings can’t be effective if you don’t make an attempt to know the person on the other side. Personal connections are a prerequisite to a healthy professional relationship.
One thing that’s always top of a manager's mind is meeting deadlines and stakeholder expectations. This can lead to a default tendency to ask for project updates whenever they meet their team.
\ Status updates are important to know how the team is progressing, what challenges they are facing, and how they plan to unblock themselves. But there’s a time and place for it. One-on-one meetings are not one of them.
\ One-on-one meetings should be largely focused on understanding your team’s concerns, discussing opportunities, brainstorming solutions, and identifying areas of growth.
\ Here are some growth-oriented questions for an effective one-on-one meeting:
\ Growth-oriented discussion will not only keep the conversation productive, but it will also be deeply engaging for your team. They will be challenged to think, deal with their problems, work out solutions, and draw valuable learning lessons thereby making them more resilient in the face of adversity and challenges.
One-on-one meetings may seem like a great opportunity to share everything on your mind—all your concerns, feedback, issues, or anything else you find worth sharing.
\ But in trying to complete your agenda, you may speak too much and do very little listening. Speaking makes you feel good—you pat yourself on the back for a job well done.
\ But doing all the talking prevents you from listening to their expectations and concerns. You not only miss understanding how your message is received but also leave them feeling unheard and misunderstood.
\ For an effective one-on-one meeting, make them feel heard and understood first. They are more likely to listen to you when you listen to them first with the intent to understand and show respect for their views.
\ Practice effective listening using these practices:
\
Go beyond the words to the non-verbal communication—tone of voice, hand gestures, and body language.
\
Acknowledge their feelings and points of view. Acknowledging does not mean that you agree with them. It simply means that you understand how they feel.
\
The key to a good one-on-one meeting is the understanding that it is the employee’s meeting rather than the manager’s meeting. This is the free-form meeting for all the pressing issues, brilliant ideas, and chronic frustrations that do not fit neatly into status reports, email, and other less personal and intimate mechanisms.
— Ben Horowitz
\ Keep the discussion flexible and open—ask what they want to discuss instead of leading with your own agenda.
\ Treating one-on-one meetings as a dialogue and not a monologue empowers your team members to take control of their own growth and learning. They feel a sense of control over the discussion which encourages them to participate with enthusiasm and a positive attitude.
Managers are busy creatures with hundreds of things on their minds and multiple things vying for their attention—stakeholders with unrealistic demands, deadlines fast approaching, customer escalations, an overflowing calendar, and an inbox full of emails.
\ This makes it hard for them to focus on one thing at a time.
\ Looking at the phone or laptop while talking.
\ Checking watch multiple times.
\ Nodding absently.
\ Fidgeting.
\ All these behaviors in a 1-1 meeting convey only one thing—that you aren’t interested in them and would much rather be elsewhere right now.
\ Being distracted, multitasking, or pretending to listen in a one-on-one meeting is not only disrespectful and rude, it instantly shuts down the conversation.
\ To get your people to open up, you need to build trust—create a space where they feel safe to share their ideas and concerns. This isn’t possible when you are present physically but mentally checked out.
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What often fails to be appreciated by everybody is just how significant an act of leadership it is, simply to give somebody your undivided attention for a while.
— Nick Robinson
\ Before entering a 1-1 conversation, commit to being present—set aside all sources of distraction and pay attention to the person in front of you.
\ Giving them your undivided attention shows the value you place in a one-on-one meeting, which encourages others to be committed and present too.
One-on-one meetings are a great source to bridge the gap between your expectations and your team’s performance. It helps both parties reach common ground by aligning on goals, setting direction, clarifying assumptions, and providing the support necessary.
\ Your team can’t excel without clarity on how they’re doing and what they can do to be better. Continuously striving for improvement is the key to unlocking their hidden potential.
\ To enable your team to strive for excellence and not settle for mediocrity, you need to have regular discussions with a pre-defined cadence. Having a schedule to meet not only makes these meetings more likely to happen, it also enables both the manager and the direct report to be better prepared for a constructive discussion.
\ You can still have impromptu meetings for feedback that can’t wait, but not everything is time-sensitive and most of it can wait.
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Managers who don’t have a plan to talk to everyone on their team regularly are deluded. They believe they are going to learn what is going on in their group through some magical organizational osmosis and they won’t. Ideas will not be discovered, talent will be ignored, and the team will slowly begin to believe what they think does not matter, and the team is the company.
— Michael Lopp
\ Depending on the size of your team, weekly, bi-weekly, monthly, or even quarterly 1-1 meetings must be set. Schedule it on the calendar to increase the likelihood of it getting attended. Don’t miss these meetings at any cost unless it’s an emergency that can’t wait.
\ 1-1 meetings aren’t a one-time phenomenon. You need to meet your team regularly. Set a rhythm so that your team looks forward to them each time.
One-on-one meetings play a crucial role in setting up a team for success by aligning goals, defining expectations, providing support, and helping your team realize their true potential.
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Taking a peek into your team’s life beyond work is important. Setting strict boundaries at work or talking work all the time prevents you from establishing a personal connection which is necessary to work together as a team. Treat your team members as humans and take an interest in their lives.
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Being responsible for meeting delivery timelines and commitments can make many managers talk about status all the time. But, asking for status updates in a 1-1 meeting destroys its purpose. One-on-one meetings should be focused on growth—what kind of opportunities your team needs, skills to be built, how to overcome challenges, etc.
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When conducting a 1-1 meeting, don’t try to tick-mark every item on your agenda. Leave space to hear your team’s thoughts and concerns. The more time you spend talking, the less you will listen. The less you listen, the less you will be able to identify changes that are necessary for your team’s productivity and performance.
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Your role as a manager will keep you busier than you’d imagined. This makes you more prone to multitasking, mind wandering, and many other distractions. Not giving the discussion your complete attention makes it hard for your team to trust you. Without trust, time is wasted on inconsequential topics without addressing the real issues and concerns.
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Trying to squeeze in a 1-1 meeting randomly when your calendar allows it may appear like the best use of your time. But doing them without proper planning makes them less likely to be effective. Ad hoc meetings are also irregular and may not happen for long. Agree on a predefined frequency with your team and schedule one-on-one meetings on the calendar upfront. Your team will look forward to them as they’ll have time to prepare for a productive conversation.
\ This story was previously published here. Follow me on LinkedIn or here for more stories.
