SShortSingh.
Back to feed

Community Member Fouad Elhamra Joins DEV With Focus on AI and ML

0
·2 views

Fouad Elhamra has introduced himself as a new member of the DEV Community platform. He expressed enthusiasm for the fields of Artificial Intelligence, Machine Learning, and Deep Learning. Elhamra shared his excitement about becoming part of the developer community. He extended his gratitude to the community for welcoming him.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

Detecting AI agents is largely a myth, security researcher argues

A security researcher argues that sophisticated AI agents using real browsers on residential connections are technically indistinguishable from human users by design. Since such agents carry genuine browser fingerprints, clean IP addresses, and real email inboxes, standard detection signals like navigator.webdriver flags can be trivially bypassed. The researcher contends that most vendors either detect only unsophisticated bots, read self-declared headers, or sell false certainty while profiting from an ongoing arms race. Rather than focusing on per-request detection, the author suggests that behavioral patterns — such as one device spanning many addresses or dozens of signups from a single subnet — are more reliable signals. The piece concludes that identity verification and traffic-shape analysis are more effective approaches than attempting direct agent detection.

0
ProgrammingDEV Community ·

AI Agents Now Pass All Bot Checks, Forcing a Rethink of Online Verification

Modern AI agents can satisfy every standard bot-detection signal — using real browsers, residential IPs, verified mailboxes, and human-like cursor movement — because they genuinely operate these tools rather than spoofing them. This means traditional checks are functioning correctly yet returning a misleading result, as they were designed to ask whether something is automated, not whether it is authorized. The author argues that 'is this automated?' has become the wrong question, since capable AI agents will always pass such tests. A more useful framing, they suggest, is 'on whose authority is this acting, and can that party be held accountable?' — shifting the paradigm from bot detection toward delegated authentication. The piece raises a practical concern for developers building signup and checkout flows: legitimate users' AI assistants may already be getting blocked, often without anyone noticing.

0
ProgrammingDEV Community ·

Developer Kills Trading Hypothesis Early by Testing Core Assumption Before Backtesting

A software developer exploring automated trading strategies proposed that undervalued small-cap stocks are more likely to be acquired via tender offer bids (TOBs), potentially generating returns. Instead of building a full backtest, he chose to first verify the core premise directly using raw data on roughly 582 real TOB cases. He compared TOB occurrence rates between an 'undervalued small-cap' group and all other listed companies, using market capitalization and price-to-book ratio below 1x as defining criteria. The analysis revealed no meaningful difference in TOB rates between the two groups, disproving the central mechanism of the hypothesis. By attacking the most uncertain assumption first, he avoided weeks of wasted development work on a fundamentally flawed strategy.

0
ProgrammingDEV Community ·

How to Manually Calculate Trainable Parameters in Neural Networks

Trainable parameters — weights and biases — are the values a neural network learns during training, and their count directly affects model complexity, memory usage, and overfitting risk. For any Dense layer, the parameter count is calculated as (input units × output units) + output units, where the output size of one layer becomes the input size of the next. This formula applies regardless of the activation function used, since functions like ReLU, Sigmoid, and Softmax perform operations but contribute zero trainable parameters. Manual calculations can be verified using TensorFlow 2.x's Keras API, where the model.summary() method displays per-layer and total parameter counts. Three worked examples with architectures of varying depth confirmed that manual calculations consistently match TensorFlow's reported totals.