TORONTO, Aug. 06, 2025 (GLOBE NEWSWIRE) -- Stack Capital Group Inc., (“Stack Capital” or the “Company”) (TSX:STCK; TSX:STCK.WT.A) today announced its financial results for the quarter ended June 30, 2025. Stack Capital reports all amounts in Canadian Dollars unless otherwise stated. Company Commentary: As at June 30, 2025, Book Value per Share (BVpS) of the Company was $14.34, compared with $12.06 as at March 31, 2025.Stack Capital had its first portfolio investment, CoreWeave (an AI hyper-scaler), go publi ...

That's a wrap!If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):12 MCP, RAG, and Agents cheat sheets for AI engineers (with visuals): ...

In the enterprise domain in particular, you guys are saying that anthropic leads in terms of market share, then open air is just behind. Just for transparency. While our audience of course, Menlow Low is on the cap table of anthropic and you have a sort of partnership with them for compute access but break down the data and how in Prop X pulled ahead in that space.Yeah, I mean, there's really three things that stood out in the report. First is, you know, Anthropic really pulled ahead in terms of enterprise ...

Company Overview - Innodata (INOD) shares closed at $50.10, approximately 29.4% below the 52-week high of $71 reached on February 21, 2025, with a year-to-date appreciation of 26.8% [1][9] - The company has outperformed competitors such as Cognizant, Infosys, and ExlService, whose shares have declined by 1.8%, 15.8%, and 4.3% respectively [2] Investment and Growth Strategy - Innodata is set to benefit from significant investments from major tech companies, including Microsoft's $80 billion and Meta Platforms' $64-$72 billion, focusing on AI technology [3] - The company plans to invest $2 million in the second quarter of 2025 to support its largest customer [3] - Expected revenues for 2025 are projected to rise by 40% year-over-year to $238.6 million, driven by growing enterprise demand and contracts with eight Big Tech firms for LLM data work [9][14] Market Position and Client Expansion - Innodata is expanding its Generative AI capabilities, targeting a market expected to be worth $200 billion by 2029 [12] - The company is enhancing its relationships with key clients, securing approximately $8 million in new engagements from four Big Tech customers [13] - New customer acquisitions are anticipated to provide significant upside to both revenue and earnings [14] Financial Performance and Valuation - The Zacks Consensus Estimate for second-quarter 2025 earnings is 11 cents per share, unchanged over the past 60 days, compared to break-even earnings in the year-ago quarter [16] - The forward 12-month Price/Sales ratio for Innodata is 5.84X, significantly higher than the Zacks Computer Services industry's 1.76X, indicating a premium valuation [19] Conclusion - Current shareholders may find justification in holding the stock due to Innodata's strong positioning in the generative AI safety domain and impressive revenue growth prospects [22] - New investors might consider waiting for a more favorable entry point as the stock remains overvalued [22]

That's a wrap!If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):A simple technique makes RAG ~32x memory efficient!- Perplexity uses it in its search index- Azure uses it in its search pipeline- HubSpot uses it in its AI assistantLet's understand how to use it in RAG systems (with code): ...

That's a wrap!If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):I have been using Jupyter Notebooks for over 9 years now.Here are 5 hidden features I discovered that most users aren't aware of: ...

Financial Data and Key Metrics Changes - Revenue for Q2 2025 reached $58.4 million, representing a year-over-year increase of 79% [6][16] - Adjusted EBITDA grew 375% to $13.2 million, reflecting an adjusted EBITDA margin of 23% compared to 9% in the same quarter last year [6][16] - Net income was $7.2 million, a significant improvement from a loss of $14,000 in the same period last year [16] - Cash increased from $56.6 million at the end of Q1 to $59.8 million at the end of Q2, with an additional $8 million collected shortly after the quarter close [7][17] Business Line Data and Key Metrics Changes - The company reported strong performance from its largest customer, generating approximately $33.9 million in revenue from this account in Q2 [33][34] - New projects with the largest customer are expected to significantly increase revenue, with forecasts indicating $10 million from another Big Tech customer in the second half of the year [10][33] Market Data and Key Metrics Changes - The company is experiencing strong demand across a diverse range of existing and new customers, positioning itself well for future growth [8][11] - The competitive landscape is shifting due to the acquisition of Scale AI by Meta, which may create new opportunities for the company [22][23] Company Strategy and Development Direction - The company is raising its full-year 2025 revenue growth guidance to 45% or more, up from 40%, based on a robust pipeline of new deals [8][57] - Investments will focus on custom annotation pipelines, agent development, and expanding global delivery capabilities, particularly for LLM testing and deployment [14][18] - The company aims to align with the growing demand for high-quality complex training data and agentic AI, which is expected to drive future growth [12][13] Management's Comments on Operating Environment and Future Outlook - Management expressed confidence in the business momentum, describing it as "nothing short of amazing" and indicating a strong outlook for the second half of the year [17][18] - The company is committed to investing in capabilities that will compound value over the next decade, despite incurring approximately $1.4 million in operating expenses viewed as investments in Q2 [14][18] Other Important Information - The company has not drawn on its $30 million credit facility, providing additional financial flexibility [7][17] - Management emphasized the importance of organic growth, distinguishing it from inorganic growth strategies employed by other companies [44] Q&A Session Summary Question: Impact of Scale AI's acquisition by Meta - Management congratulated Scale AI and noted that their success highlights the importance of data in model performance, suggesting that the shift in focus could accelerate market opportunities for the company [22][23] Question: Timing of business shifts post-acquisition - Management indicated that they are actively engaging with market participants and have stepped up outreach efforts, anticipating exciting opportunities in the coming months [24] Question: Variance in revenue expectations - Management clarified that previous guidance was cautious due to dependencies on engineering teams, but they are optimistic about the current pipeline and opportunities [31][34] Question: Go-to-market strategy for enterprises - Management stated that they are already targeting enterprises and finding receptivity across various markets, with a focus on integrating new technologies into workflows [36][38] Question: Future investment scale - Management plans to increase investments in sales and delivery capabilities by approximately $1.5 million in Q3, capitalizing on significant market opportunities [39][40] Question: Organic growth and competitive pressures - Management highlighted that while there is competitive pressure, the quality of their data and services is the primary focus for customers, making them less price-sensitive [46][47] Question: Revenue opportunities and customer relationships - Management confirmed that there is a larger pipeline of opportunities compared to the previous quarter, with many projects progressing well [48][49]

Q2 2025 Financial Results July 30, 2025 1 Forward-looking Statements 2 This presentation contains forward-looking statements within the meaning of the federal securities laws. Forward-looking statements include but are not limited to statements relating to: our financial guidance for the third quarter of 2025 and underlying assumptions; our customer relationship flywheel; the impact of AI, ML, and LLMs; our ability to become a leader in agentic shopping; our product development investments and marketing ini ...

I'm so excited to be here. Well, actually, I'm not. I mean, I'm excited, but I'm mostly nervous and I'm scared to mess up.I mean, maybe I just did. But how come it's so easy for me to lie to you. And how come we're often incentivized to lie to each other.I think one problem is that talk is cheap. Incidentally, this also a type of lies and cheap data that we train our current artificial intelligence on, namely large language models. This is also why we say that actions speak louder than words.But if that's t ...

Core Problem & Solution - Vertical AI applications face a "last mile problem" in understanding industry-specific context and workflows, which is more critical than model sophistication [4][6] - Anterior proposes an "adaptive domain intelligence engine" to convert customer-specific domain insights into performance improvements [17] - The engine consists of measurement (performance evaluation) and improvement (iterative refinement) components [17] Measurement & Metrics - Defining key performance metrics that users care about is crucial, such as minimizing false approvals in healthcare or preventing dollar loss from fraud [18][19][20] - Developing a failure mode ontology helps categorize and analyze different ways the AI can fail, enabling targeted improvements [21][22] - Combining metric tracking with failure mode analysis allows prioritization of development efforts based on the impact on key metrics [26][27] Iteration & Improvement - Failure mode labeling creates ready-made datasets for iterative model improvement, using production data to ensure relevance [29] - Domain experts can suggest changes to the application pipeline and provide new domain knowledge to enhance performance [32][33] - This process enables rapid iteration, potentially fixing issues the same day by adding relevant domain knowledge and validating with evals [37] Domain Expertise - The level of domain expertise required depends on the specific workflow and optimization goals, with clinical reasoning requiring experienced doctors [38][39] - Bespoke tooling is recommended for integrating domain expert feedback into the platform and workflows [41] - Domain expert reviews provide performance metrics, failure modes, and suggested improvements, all in one [38] Results & Performance - Anterior achieved a 95% accuracy baseline in approving care requests, which was further improved to 99% through iterative refinement using the described system [14][15]