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Diginex Announces MOU for US$305m Acquisition of Findings, a leading cybersecurity and compliance automation company
Globenewswire· 2025-08-12 20:05
Core Viewpoint - Diginex Limited has signed a non-binding Memorandum of Understanding (MOU) to acquire 100% of IDRRA Cyber Security Ltd. (Findings) for a total consideration of up to US$305 million, aiming to enhance its technological capabilities in the cybersecurity sector and expand its compliance data verification and regulatory compliance automation offerings [1][3][5]. Group 1: Acquisition Details - The acquisition consideration includes US$270 million in Diginex shares and up to US$35 million in cash, with US$20 million contingent on achieving certain financial targets [3]. - The share consideration will be based on the 60-business day trailing VWAP of Diginex's shares as of the MOU signing date, with customary lock-up periods for Findings' shareholders ranging from 9 to 18 months [3]. - Diginex will provide further growth funding to Findings post-closing based on agreed performance metrics to support its innovation and global expansion [4]. Group 2: Strategic Importance - The acquisition aligns with Diginex's mission to enhance its supply chain risk management and compliance offerings, leveraging Findings' expertise in AI vendor risk management and cloud security [2][5]. - Findings specializes in automated vendor risk management and continuous monitoring, which will complement Diginex's existing platforms like diginexESG, diginexLUMEN, and diginexAPPRISE [2][5]. - The transaction is expected to enable Findings to leverage Diginex's global reach and resources to enhance its impact in securing supply chains against evolving threats [5][9]. Group 3: Exclusivity and Next Steps - The MOU includes a 45-day exclusivity period during which Findings will not engage with other potential acquirers, indicating a strong commitment from both parties to finalize the transaction [6].
Jensen Huang on AI Agents That Talk to Your Data | Conversation with Alex Bouzari
DDN· 2025-08-12 18:44
It's a new way of interacting with your company's data. You know, instead of retrieving data, you figure out what's in it. You maybe modify it and store it back.You're in a lot of ways talking to your company's data. >> Yeah. You have questions for your company's data. Your company's data speaks back to you, >> tells you what you need to know.uh you might have a fair amount of insight that's uh distributed in your company's raw data that is now in this uh semantic form and uh you would like to have agents A ...
GPT-5: Our best model for work
OpenAI· 2025-08-07 18:01
Model Capabilities - GPT5 adjusts reasoning effort based on the task, eliminating the need to select a specific model [1] - GPT5 excels at data analysis, including identifying trends and themes from messy data [2][3] - GPT5 demonstrates improved coding capabilities, particularly in front-end development, enabling rapid prototyping [3][6] - GPT5 refines writing style and respects guidance on tone, reducing errors [5] - GPT5 offers suggestions for next steps and potential improvements [4][7] Workflow & Productivity - GPT5 facilitates the entire process from customer feedback analysis to building a working prototype [7] - GPT5 provides clear explanations of its methodology to ensure trust in its conclusions [4] - GPT5 can generate product requirements documents and user stories [4] - GPT5 can create interactive prototypes from a single prompt [5][6] - GPT5 delivers reliable and thorough answers, including executive summaries with charts and tables [8]
How to look at your data — Jeff Huber (Choma) + Jason Liu (567)
AI Engineer· 2025-08-06 16:22
Retrieval System Evaluation - Industry should prioritize fast and inexpensive evaluations (fast evals) using query and document pairs to enable rapid experimentation [7] - Industry can leverage LLMs to generate queries, but should focus on aligning synthetic queries with real-world user queries to avoid misleading results [9][11] - Industry can empirically validate the performance of new embedding models on specific data using fast evals, rather than relying solely on public benchmarks like MTeb [12] - Weights & Biases chatbot analysis reveals that the original embedding model (text embedding three small) performed the worst, while voyage 3 large model performed the best, highlighting the importance of data-driven evaluation [17][18] Output Analysis and Product Development - Industry should extract structured data from user conversations (summaries, tools used, errors, satisfaction, frustration) to identify patterns and inform product development [28][29] - Industry can use extracted metadata to find clusters and identify segments for targeted improvements, similar to how marketing uses user segmentation [29][26] - Cura library enables summarization, clustering, and aggregation of conversations to compare evals across different KPIs, helping to identify areas for improvement [32] - Industry should focus on providing the right infrastructure and tools to support AI agents, rather than solely focusing on improving the AI itself [39] - Industry should define evals, find clusters, and compare KPIs across clusters to make informed decisions on what to build, fix, and ignore [40][41] - Industry should monitor query types and performance over time to understand how the product is being used and identify opportunities for improvement [45]
X @The Economist
The Economist· 2025-08-05 23:40
Demographics Analysis - The analysis covers the first names of approximately 400 million people born in two countries [1] - The findings from the analysis are noteworthy [1]
Accelerant just went public. CEO Jeff Radke breaks down what the company does
CNBC Television· 2025-07-24 19:45
A big public debut today. The insurance risk exchange company Accelerant beginning to trade. It's a platform where specialty insurance underwriters can sell insurance premiums to buyers like insurance or reinsurance companies, institutional investors even.You can kind of see the flow there. They also provide data to make underwriting for niche businesses more efficient. IPO is met with strong demand.The company priced above its $18 to $20 range at 21, upsized the offering. Shares started trading at 28.50% l ...
X @Balaji
Balaji· 2025-07-22 14:58
One thought btw is that I disagree with Google’s chosen example. AI “personalization” of an outbound email can feel impersonal and spammy. It’s at best a first draft that you completely rewrite.But as a tool for data analysis, this is amazing.Balaji (@balajis):Amazing. First really useful AI integration by Google. https://t.co/ASKCWRZaxM ...
X @Avi Chawla
Avi Chawla· 2025-07-16 18:55
Productivity Enhancement - Gemini can complete 6 hours of manual data analysis work in just 2 minutes [1] - Gemini in Colab enables data planning, analysis, and visualization without writing code [1] - The tool processed a dataset with 100 thousand rows hands-off [1]
X @Avi Chawla
Avi Chawla· 2025-07-16 06:33
Check this!!Gemini can do 6 hours of manual data analysis work in just 2 mins.Gemini in Colab lets you plan, analyze, and visualize your data. I used a dataset with 100k rows and didn't write or execute a single line of code.Completely hands-off! https://t.co/kb7YuIvnVO ...
X @Bankless
Bankless· 2025-07-14 17:46
Problem Statement - Tokenholders are underserved due to fragmented Ethereum data, inconsistent metrics, and noise from hype and analyst opinions [1] Solution - The ETH Report provides standardized, no-spin data with fundamentals, core KPIs, and quarter-to-quarter explanations for ETH holders [1] Key Findings of Q2 - The report reveals insights from Q2, accessible via a link to download the report and access 10 unique dashboards covering the ecosystem [1]