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I got fired for poor performance. What should i say in my next job interviews?
- Performance in one role at one company doesn’t define you. Behavioral interviews often explore failures, seeing them as growth steps.
- If you’ve faced setbacks, frame them as learning experiences—but don’t admit to being fired. When asked why you left, avoid saying “I was terminated.” Instead, say, “My goals and priorities shifted, and I sought a new challenge”—technically true. The best outcome? It doesn’t come up.
- You’re a candidate, there’s a job opening, and you’re ready to start. Focus on what you bring to the table, not past missteps. Keep the conversation about your value, not your exit.
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The DeepSeek Hype from the Perspective of an AI Hobbyist
- DeepSeek has been hyped by numerous posts claiming its open-source nature, but it's only available with a research paper - not fully open-source.
- Huggingface is one AI company attempting to rebuild the missing parts of DeepSeek's R1 training pipeline, but none have replicated their results yet.
- DeepSeek's claims about its groundbreaking FP8 technology and MoE technology are highly misleading.
- FP8 has been around for years, and MoE has been used as a method to speed up LLMs since its release with Mixtral.
- Confusingly, reasoning versions of DeepSeek's competitors' models have been misrepresented as smaller versions of DeepSeek-R1.
- DeepSeek-R1 is the first to use large-scale mixed-precision FP8 in creating a cutting-edge model with its DualPipe algorithm and Multi-Token Prediction for great developments in parallelism.
- The article is not meant to discredit the DeepSeek team's advancements in technology and shouldn't be misrepresented to gain attention.
- However, the ability of DeepSeek to run on Huawei's NPUs due to export restrictions is a matter of concern for American companies.
- Despite the hype, a full R1 model requires a lot more resources than any consumer GPU can provide.
- Misrepresentation of AI advancements can undermine researchers' hard work and hurt progress in the field.
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The Open-Source Revolution in AI: Democratizing Access and Expertise
- The rise of open-source large language models (LLMs) like DeepSeek-R1 (DeepSeek AI, 2023), coupled with advancements in training methodologies like GRPO (Aguilera, 2025c), marks a pivotal moment in AI.
- Open-source models reshape research, collaboration, and accessibility in the field of AI, democratizing access to powerful technology and fostering a collaborative ecosystem that promises to accelerate innovation and address critical ethical considerations.
- Practical experiments conducted with DeepSeek-R1, a powerful and accessible open-source LLM, explored the model’s capabilities across diverse domains and demonstrated that this type of technology can provide nuanced, context-aware, and holistic guidance to individuals.
- Accessibility through platforms like Ollama and Google Colab democratizes AI by lowering the barrier to entry for individuals and smaller organizations, fostering a more inclusive and collaborative AI ecosystem.
- The open-source nature of DeepSeek-R1 facilitates a crucial synergy between research and engineering. This rapid iteration accelerates the pace of innovation in the field, allowing developers and researchers worldwide to contribute to the improvement of the model, identify potential biases, and enhance its capabilities.
- One of the most significant advantages of open-source LLMs like DeepSeek-R1 is their cost-effectiveness compared to commercial alternatives.
- GRPO (Aguilera, 2025c) plays a crucial role in this open-source revolution, significantly reducing costs and democratizing access to advanced AI capabilities.
- However, the democratization of AI also presents challenges to the open-source community, such as the potential misuse of these models to generate misinformation or develop biased AI systems. To address this issue, the community must develop robust ethical guidelines and safeguards to ensure these powerful tools are used responsibly and benefit humanity.
- The open-source AI community should prioritize developing robust ethical guidelines and safeguards to ensure these powerful tools are used responsibly and benefit humanity. Further research should focus on improving training efficiency, enhancing model explainability, and addressing the potential biases inherent in large datasets.
- The dawn of open-source AI has arrived, and its future trajectory will be shaped by the collaborative efforts of researchers, developers, and the broader community.
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Using Data Analysis for Tiger Tracing, Tagging, and Saving Lives in Arunachal Pradesh
- Leveraging data analysis techniques can help address the conflict between humans and tigers in Arunachal Pradesh
- Data from GPS collars and camera traps can be analyzed to monitor tiger movements and predict high-risk areas
- Tagging tigers with GPS-enabled collars provides valuable data for identifying risky zones and formulating preventive strategies
- Involving local communities, educating them about tiger behavior, and implementing data-driven initiatives can foster coexistence
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Minis
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Engineering student develops AI model to translate sign language for those with speech and hearing impairment
- A 19-year-old BTech student, Preeyaj Safary, has developed an AI model that translates sign language into text, making communication easier for individuals with hearing and speech impairments.
- The AI model captures signs using the camera and compares them with pre-filled images in the database to show the meaning and accuracy level of the translation.
- The technology was developed by Safary after seeing the difficulties faced by hearing and speech-impaired individuals in communicating with the public.
- Safary's work highlights the potential of technology in promoting inclusivity in society and addressing communication challenges faced by marginalized communities.
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Researchers use AI to predict what a mouse sees by decoding brain signals
- Swiss researchers at EPFL have developed a machine-learning algorithm named 'CEBRA', which can reveal the hidden structure in data recorded from the brain.
- The 'CEBRA' algorithm was demonstrated by the researchers by reconstructing the images of a black-and-white film that a mouse watched, based on the recorded brain data.
- The tool can also predict the movements of the arm in primates, as per the study conducted by the EPFL researchers.
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US scientists use brain scans and AI to interpret human thoughts
- US scientists have discovered a way to use brain scans and AI to interpret human thoughts.
- In a study, three participants spent 16 hours inside an fMRI machine while their responses to words and meanings were mapped out.
- This data was fed into a GPT-1 AI model, which was able to transcribe the essence of their thoughts.
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Why Narayana Murthy turned down Air India & 2 other jobs to join IIM at half the salary
- Infosys Founder Narayana Murthy was invited as the Chief Guest at IIM Ahmedabad's 58th convocation.
- IIM-A revealed that Murthy had three job offers from TELCO, TISCO, and Air India which offered him a salary of ₹1,600/month, but he chose to join IIM-A as Chief Systems Programmer at ₹800/month.
- The reason for his decision was that IIM-A was planning to install India's first time-sharing system, which he wanted to learn about.
- Murthy later went on to become the Chairperson of IIM-A, showcasing his long-standing association with the prestigious institute.
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UBS-Credit Suisse merger to result in up to 36,000 job cuts worldwide: Reports
- The Swiss government hastily arranged the takeover of Credit Suisse by UBS on March 19 to prevent a global financial meltdown.
- The merger could result in up to 36,000 job cuts worldwide, with up to 11,000 in Switzerland alone.
- UBS announced the appointment of former CEO Sergio Ermotti to handle the risks involved in the merger.
- Credit Suisse was embroiled in a series of scandals before the merger, including the bankruptcy of Greensill and the implosion of Archegos, a bribery scandal in Mozambique, and a money laundering case linked to a Bulgarian cocaine network.
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Indian startup ecosystem sees no new unicorn in 6 months, a first in 7 yrs: Report
- India's startup ecosystem has not seen a new unicorn in the last six months, according to a report by Moneycontrol.
- This marks the longest period of time in seven years that the sector has gone without a new startup valued at $1 billion or more.
- The last Indian startup to achieve unicorn status was Molbio Diagnostics in September 2022.
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