#Black box algorithms

[appendingfic]

Can't believe we're at the "Man grew complacent and lazy, making the robots do all the work" part of the story but (a) by "Man" we mean "The Man", and (b) the robots aren't even sentient

[jcmarchi]

The Black Box Problem in LLMs: Challenges and Emerging Solutions

New Post has been published on https://thedigitalinsider.com/the-black-box-problem-in-llms-challenges-and-emerging-solutions/

The Black Box Problem in LLMs: Challenges and Emerging Solutions

Machine learning, a subset of AI, involves three components: algorithms, training data, and the resulting model. An algorithm, essentially a set of procedures, learns to identify patterns from a large set of examples (training data). The culmination of this training is a machine-learning model. For example, an algorithm trained with images of dogs would result in a model capable of identifying dogs in images.

Black Box in Machine Learning

In machine learning, any of the three components—algorithm, training data, or model—can be a black box. While algorithms are often publicly known, developers may choose to keep the model or the training data secretive to protect intellectual property. This obscurity makes it challenging to understand the AI’s decision-making process.

AI black boxes are systems whose internal workings remain opaque or invisible to users. Users can input data and receive output, but the logic or code that produces the output remains hidden. This is a common characteristic in many AI systems, including advanced generative models like ChatGPT and DALL-E 3.

LLMs such as GPT-4 present a significant challenge: their internal workings are largely opaque, making them “black boxes”.  Such opacity isn’t just a technical puzzle; it poses real-world safety and ethical concerns. For instance, if we can’t discern how these systems reach conclusions, can we trust them in critical areas like medical diagnoses or financial assessments?

The Scale and Complexity of LLMs

The scale of these models adds to their complexity. Take GPT-3, for instance, with its 175 billion parameters, and newer models having trillions. Each parameter interacts in intricate ways within the neural network, contributing to emergent capabilities that aren’t predictable by examining individual components alone. This scale and complexity make it nearly impossible to fully grasp their internal logic, posing a hurdle in diagnosing biases or unwanted behaviors in these models.

The Tradeoff: Scale vs. Interpretability

Reducing the scale of LLMs could enhance interpretability but at the cost of their advanced capabilities. The scale is what enables behaviors that smaller models cannot achieve. This presents an inherent tradeoff between scale, capability, and interpretability.

Impact of the LLM Black Box Problem

1. Flawed Decision Making

The opaqueness in the decision-making process of LLMs like GPT-3 or BERT can lead to undetected biases and errors. In fields like healthcare or criminal justice, where decisions have far-reaching consequences, the inability to audit LLMs for ethical and logical soundness is a major concern. For example, a medical diagnosis LLM relying on outdated or biased data can make harmful recommendations. Similarly, LLMs in hiring processes may inadvertently perpetuate gender bi ases. The black box nature thus not only conceals flaws but can potentially amplify them, necessitating a proactive approach to enhance transparency.

2. Limited Adaptability in Diverse Contexts

The lack of insight into the internal workings of LLMs restricts their adaptability. For example, a hiring LLM might be inefficient in evaluating candidates for a role that values practical skills over academic qualifications, due to its inability to adjust its evaluation criteria. Similarly, a medical LLM might struggle with rare disease diagnoses due to data imbalances. This inflexibility highlights the need for transparency to re-calibrate LLMs for specific tasks and contexts.

3. Bias and Knowledge Gaps

LLMs’ processing of vast training data is subject to the limitations imposed by their algorithms and model architectures. For instance, a medical LLM might show demographic biases if trained on unbalanced datasets. Also, an LLM’s proficiency in niche topics could be misleading, leading to overconfident, incorrect outputs. Addressing these biases and knowledge gaps requires more than just additional data; it calls for an examination of the model’s processing mechanics.

4. Legal and Ethical Accountability

The obscure nature of LLMs creates a legal gray area regarding liability for any harm caused by their decisions. If an LLM in a medical setting provides faulty advice leading to patient harm, determining accountability becomes difficult due to the model’s opacity. This legal uncertainty poses risks for entities deploying LLMs in sensitive areas, underscoring the need for clear governance and transparency.

5. Trust Issues in Sensitive Applications

For LLMs used in critical areas like healthcare and finance, the lack of transparency undermines their trustworthiness. Users and regulators need to ensure that these models do not harbor biases or make decisions based on unfair criteria. Verifying the absence of bias in LLMs necessitates an understanding of their decision-making processes, emphasizing the importance of explainability for ethical deployment.

6. Risks with Personal Data

LLMs require extensive training data, which may include sensitive personal information. The black box nature of these models raises concerns about how this data is processed and used. For instance, a medical LLM trained on patient records raises questions about data privacy and usage. Ensuring that personal data is not misused or exploited requires transparent data handling processes within these models.

Emerging Solutions for Interpretability

To address these challenges, new techniques are being developed. These include counterfactual (CF) approximation methods. The first method involves prompting an LLM to change a specific text concept while keeping other concepts constant. This approach, though effective, is resource-intensive at inference time.

The second approach involves creating a dedicated embedding space guided by an LLM during training. This space aligns with a causal graph and helps identify matches approximating CFs. This method requires fewer resources at test time and has been shown to effectively explain model predictions, even in LLMs with billions of parameters.

These approaches highlight the importance of causal explanations in NLP systems to ensure safety and establish trust. Counterfactual approximations provide a way to imagine how a given text would change if a certain concept in its generative process were different, aiding in practical causal effect estimation of high-level concepts on NLP models.

Deep Dive: Explanation Methods and Causality in LLMs

Probing and Feature Importance Tools

Probing is a technique used to decipher what internal representations in models encode. It can be either supervised or unsupervised and is aimed at determining if specific concepts are encoded at certain places in a network. While effective to an extent, probes fall short in providing causal explanations, as highlighted by Geiger et al. (2021).

Feature importance tools, another form of explanation method, often focus on input features, although some gradient-based methods extend this to hidden states. An example is the Integrated Gradients method, which offers a causal interpretation by exploring baseline (counterfactual, CF) inputs. Despite their utility, these methods still struggle to connect their analyses with real-world concepts beyond simple input properties.

Intervention-Based Methods

Intervention-based methods involve modifying inputs or internal representations to study effects on model behavior. These methods can create CF states to estimate causal effects, but they often generate implausible inputs or network states unless carefully controlled. The Causal Proxy Model (CPM), inspired by the S-learner concept, is a novel approach in this realm, mimicking the behavior of the explained model under CF inputs. However, the need for a distinct explainer for each model is a major limitation.

Approximating Counterfactuals

Counterfactuals are widely used in machine learning for data augmentation, involving perturbations to various factors or labels. These can be generated through manual editing, heuristic keyword replacement, or automated text rewriting. While manual editing is accurate, it’s also resource-intensive. Keyword-based methods have their limitations, and generative approaches offer a balance between fluency and coverage.

Faithful Explanations

Faithfulness in explanations refers to accurately depicting the underlying reasoning of the model. There’s no universally accepted definition of faithfulness, leading to its characterization through various metrics like Sensitivity, Consistency, Feature Importance Agreement, Robustness, and Simulatability. Most of these methods focus on feature-level explanations and often conflate correlation with causation. Our work aims to provide high-level concept explanations, leveraging the causality literature to propose an intuitive criterion: Order-Faithfulness.

We’ve delved into the inherent complexities of LLMs, understanding their ‘black box’ nature and the significant challenges it poses. From the risks of flawed decision-making in sensitive areas like healthcare and finance to the ethical quandaries surrounding bias and fairness, the need for transparency in LLMs has never been more evident.

The future of LLMs and their integration into our daily lives and critical decision-making processes hinges on our ability to make these models not only more advanced but also more understandable and accountable. The pursuit of explainability and interpretability is not just a technical endeavor but a fundamental aspect of building trust in AI systems. As LLMs become more integrated into society, the demand for transparency will grow, not just from AI practitioners but from every user who interacts with these systems.

[nicolae]

Threats of Artificial Intelligence for Cybersecurity

Sfetcu, Nicolae (2024), Threats of Artificial Intelligence for Cybersecurity, IT & C, 3:3, ppp,   Abstract Artificial intelligence enables automated decision-making and facilitates many aspects of daily life, bringing with it improvements in operations and numerous other benefits. However, AI systems face numerous cybersecurity threats, and AI itself needs to be secured, as cases of malicious…

[algos11]

Algorithmic trading, also known as automated trading or black-box trading, has revolutionised the world of futures and options trading. 

[hmatrading]

Are you tired of manually analyzing market trends and making trading decisions? Are you ready to embrace the power of technology to enhance your trading game? Look no further than algo trading software

[catboybiologist]

Many billionaires in tech bros warn about the dangerous of AI. It's pretty obviously not because of any legitimate concern that AI will take over. But why do they keep saying stuff like this then? Why do we keep on having this still fear of some kind of singularity style event that leads to machine takeover?

The possibility of a self-sufficient AI taking over in our lifetimes is... Basically nothing, if I'm being honest. I'm not an expert by any means, I've used ai powered tools in my biology research, and I'm somewhat familiar with both the limits and possibility of what current models have to offer.

I'm starting to think that the reason why billionaires in particular try to prop this fear up is because it distracts from the actual danger of ai: the fact that billionaires and tech mega corporations have access to data, processing power, and proprietary algorithms to manipulate information on mass and control the flow of human behavior. To an extent, AI models are a black box. But the companies making them still have control over what inputs they receive for training and analysis, what kind of outputs they generate, and what they have access to. They're still code. Just some of the logic is built on statistics from large datasets instead of being manually coded.

The more billionaires make AI fear seem like a science fiction concept related to conciousness, the more they can absolve themselves in the eyes of public from this. The sheer scale of the large model statistics they're using, as well as the scope of surveillance that led to this point, are plain to see, and I think that the companies responsible are trying to play a big distraction game.

Hell, we can see this in the very use of the term artificial intelligence. Obviously, what we call artificial intelligence is nothing like science fiction style AI. Terms like large statistics, large models, and hell, even just machine learning are far less hyperbolic about what these models are actually doing.

I don't know if your average Middle class tech bro is actively perpetuating this same thing consciously, but I think the reason why it's such an attractive idea for them is because it subtly inflates their ego. By treating AI as a mystical act of the creation, as trending towards sapience or consciousness, if modern AI is just the infant form of something grand, they get to feel more important about their role in the course of society. Admitting the actual use and the actual power of current artificial intelligence means admitting to themselves that they have been a tool of mega corporations and billionaires, and that they are not actually a major player in human evolution. None of us are, but it's tech bro arrogance that insists they must be.

Do most tech bros think this way? Not really. Most are just complict neolibs that don't think too hard about the consequences of their actions. But for the subset that do actually think this way, this arrogance is pretty core to their thinking.

Obviously this isn't really something I can prove, this is just my suspicion from interacting with a fair number of techbros and people outside of CS alike.

[funeral]

In a world controlled by algorithms, the human being gradually loses the power to act, loses autonomy. The human being confronts a world that resists efforts at comprehension. He or she obeys algorithmic decisions, which lack transparency. Algorithms become black boxes. The world is lost in the deep layers of neuronal networks to which human beings have no access.

Byung-Chul Han, Non-things: Upheaval in the Lifeworld

[jcmarchi]

Ravi Bommakanti, CTO of App Orchid – Interview Series

New Post has been published on https://thedigitalinsider.com/ravi-bommakanti-cto-of-app-orchid-interview-series/

Ravi Bommakanti, CTO of App Orchid – Interview Series

Ravi Bommakanti, Chief Technology Officer at App Orchid, leads the company’s mission to help enterprises operationalize AI across applications and decision-making processes. App Orchid’s flagship product, Easy Answers™, enables users to interact with data using natural language to generate AI-powered dashboards, insights, and recommended actions.

The platform integrates structured and unstructured data—including real-time inputs and employee knowledge—into a predictive data fabric that supports strategic and operational decisions. With in-memory Big Data technology and a user-friendly interface, App Orchid streamlines AI adoption through rapid deployment, low-cost implementation, and minimal disruption to existing systems.

Let’s start with the big picture—what does “agentic AI” mean to you, and how is it different from traditional AI systems?

Agentic AI represents a fundamental shift from the static execution typical of traditional AI systems to dynamic orchestration. To me, it’s about moving from rigid, pre-programmed systems to autonomous, adaptable problem-solvers that can reason, plan, and collaborate.

What truly sets agentic AI apart is its ability to leverage the distributed nature of knowledge and expertise. Traditional AI often operates within fixed boundaries, following predetermined paths. Agentic systems, however, can decompose complex tasks, identify the right specialized agents for sub-tasks—potentially discovering and leveraging them through agent registries—and orchestrate their interaction to synthesize a solution. This concept of agent registries allows organizations to effectively ‘rent’ specialized capabilities as needed, mirroring how human expert teams are assembled, rather than being forced to build or own every AI function internally.

So, instead of monolithic systems, the future lies in creating ecosystems where specialized agents can be dynamically composed and coordinated – much like a skilled project manager leading a team – to address complex and evolving business challenges effectively.

How is Google Agentspace accelerating the adoption of agentic AI across enterprises, and what’s App Orchid’s role in this ecosystem?

Google Agentspace is a significant accelerator for enterprise AI adoption. By providing a unified foundation to deploy and manage intelligent agents connected to various work applications, and leveraging Google’s powerful search and models like Gemini, Agentspace enables companies to transform siloed information into actionable intelligence through a common interface.

App Orchid acts as a vital semantic enablement layer within this ecosystem. While Agentspace provides the agent infrastructure and orchestration framework, our Easy Answers platform tackles the critical enterprise challenge of making complex data understandable and accessible to agents. We use an ontology-driven approach to build rich knowledge graphs from enterprise data, complete with business context and relationships – precisely the understanding agents need.

This creates a powerful synergy: Agentspace provides the robust agent infrastructure and orchestration capabilities, while App Orchid provides the deep semantic understanding of complex enterprise data that these agents require to operate effectively and deliver meaningful business insights. Our collaboration with the Google Cloud Cortex Framework is a prime example, helping customers drastically reduce data preparation time (up to 85%) while leveraging our platform’s industry-leading 99.8% text-to-SQL accuracy for natural language querying. Together, we empower organizations to deploy agentic AI solutions that truly grasp their business language and data intricacies, accelerating time-to-value.

What are real-world barriers companies face when adopting agentic AI, and how does App Orchid help them overcome these?

The primary barriers we see revolve around data quality, the challenge of evolving security standards – particularly ensuring agent-to-agent trust – and managing the distributed nature of enterprise knowledge and agent capabilities.

Data quality remains the bedrock issue. Agentic AI, like any AI, provides unreliable outputs if fed poor data. App Orchid tackles this foundationally by creating a semantic layer that contextualizes disparate data sources. Building on this, our unique crowdsourcing features within Easy Answers engage business users across the organization—those who understand the data’s meaning best—to collaboratively identify and address data gaps and inconsistencies, significantly improving reliability.

Security presents another critical hurdle, especially as agent-to-agent communication becomes common, potentially spanning internal and external systems. Establishing robust mechanisms for agent-to-agent trust and maintaining governance without stifling necessary interaction is key. Our platform focuses on implementing security frameworks designed for these dynamic interactions.

Finally, harnessing distributed knowledge and capabilities effectively requires advanced orchestration. App Orchid leverages concepts like the Model Context Protocol (MCP), which is increasingly pivotal. This enables the dynamic sourcing of specialized agents from repositories based on contextual needs, facilitating fluid, adaptable workflows rather than rigid, pre-defined processes. This approach aligns with emerging standards, such as Google’s Agent2Agent protocol, designed to standardize communication in multi-agent systems. We help organizations build trusted and effective agentic AI solutions by addressing these barriers.

Can you walk us through how Easy Answers™ works—from natural language query to insight generation?

Easy Answers transforms how users interact with enterprise data, making sophisticated analysis accessible through natural language. Here’s how it works:

Connectivity: We start by connecting to the enterprise’s data sources – we support over 200 common databases and systems. Crucially, this often happens without requiring data movement or replication, connecting securely to data where it resides.

Ontology Creation: Our platform automatically analyzes the connected data and builds a comprehensive knowledge graph. This structures the data into business-centric entities we call Managed Semantic Objects (MSOs), capturing the relationships between them.

Metadata Enrichment: This ontology is enriched with metadata. Users provide high-level descriptions, and our AI generates detailed descriptions for each MSO and its attributes (fields). This combined metadata provides deep context about the data’s meaning and structure.

Natural Language Query: A user asks a question in plain business language, like “Show me sales trends for product X in the western region compared to last quarter.”

Interpretation & SQL Generation: Our NLP engine uses the rich metadata in the knowledge graph to understand the user’s intent, identify the relevant MSOs and relationships, and translate the question into precise data queries (like SQL). We achieve an industry-leading 99.8% text-to-SQL accuracy here.

Insight Generation (Curations): The system retrieves the data and determines the most effective way to present the answer visually. In our platform, these interactive visualizations are called ‘curations’. Users can automatically generate or pre-configure them to align with specific needs or standards.

Deeper Analysis (Quick Insights): For more complex questions or proactive discovery, users can leverage Quick Insights. This feature allows them to easily apply ML algorithms shipped with the platform to specified data fields to automatically detect patterns, identify anomalies, or validate hypotheses without needing data science expertise.

This entire process, often completed in seconds, democratizes data access and analysis, turning complex data exploration into a simple conversation.

How does Easy Answers bridge siloed data in large enterprises and ensure insights are explainable and traceable?

Data silos are a major impediment in large enterprises. Easy Answers addresses this fundamental challenge through our unique semantic layer approach.

Instead of costly and complex physical data consolidation, we create a virtual semantic layer. Our platform builds a unified logical view by connecting to diverse data sources where they reside. This layer is powered by our knowledge graph technology, which maps data into Managed Semantic Objects (MSOs), defines their relationships, and enriches them with contextual metadata. This creates a common business language understandable by both humans and AI, effectively bridging technical data structures (tables, columns) with business meaning (customers, products, sales), regardless of where the data physically lives.

Ensuring insights are trustworthy requires both traceability and explainability:

Traceability: We provide comprehensive data lineage tracking. Users can drill down from any curations or insights back to the source data, viewing all applied transformations, filters, and calculations. This provides full transparency and auditability, crucial for validation and compliance.

Explainability: Insights are accompanied by natural language explanations. These summaries articulate what the data shows and why it’s significant in business terms, translating complex findings into actionable understanding for a broad audience.

This combination bridges silos by creating a unified semantic view and builds trust through clear traceability and explainability.

How does your system ensure transparency in insights, especially in regulated industries where data lineage is critical?

Transparency is absolutely non-negotiable for AI-driven insights, especially in regulated industries where auditability and defensibility are paramount. Our approach ensures transparency across three key dimensions:

Data Lineage: This is foundational. As mentioned, Easy Answers provides end-to-end data lineage tracking. Every insight, visualization, or number can be traced back meticulously through its entire lifecycle—from the original data sources, through any joins, transformations, aggregations, or filters applied—providing the verifiable data provenance required by regulators.

Methodology Visibility: We avoid the ‘black box’ problem. When analytical or ML models are used (e.g., via Quick Insights), the platform clearly documents the methodology employed, the parameters used, and relevant evaluation metrics. This ensures the ‘how’ behind the insight is as transparent as the ‘what’.

Natural Language Explanation: Translating technical outputs into understandable business context is crucial for transparency. Every insight is paired with plain-language explanations describing the findings, their significance, and potentially their limitations, ensuring clarity for all stakeholders, including compliance officers and auditors.

Furthermore, we incorporate additional governance features for industries with specific compliance needs like role-based access controls, approval workflows for certain actions or reports, and comprehensive audit logs tracking user activity and system operations. This multi-layered approach ensures insights are accurate, fully transparent, explainable, and defensible.

How is App Orchid turning AI-generated insights into action with features like Generative Actions?

Generating insights is valuable, but the real goal is driving business outcomes. With the correct data and context, an agentic ecosystem can drive actions to bridge the critical gap between insight discovery and tangible action, moving analytics from a passive reporting function to an active driver of improvement.

Here’s how it works: When the Easy Answers platform identifies a significant pattern, trend, anomaly, or opportunity through its analysis, it leverages AI to propose specific, contextually relevant actions that could be taken in response.

These aren’t vague suggestions; they are concrete recommendations. For instance, instead of just flagging customers at high risk of churn, it might recommend specific retention offers tailored to different segments, potentially calculating the expected impact or ROI, and even drafting communication templates. When generating these recommendations, the system considers business rules, constraints, historical data, and objectives.

Crucially, this maintains human oversight. Recommended actions are presented to the appropriate users for review, modification, approval, or rejection. This ensures business judgment remains central to the decision-making process while AI handles the heavy lifting of identifying opportunities and formulating potential responses.

Once an action is approved, we can trigger an agentic flow for seamless execution through integrations with operational systems. This could mean triggering a workflow in a CRM, updating a forecast in an ERP system, launching a targeted marketing task, or initiating another relevant business process – thus closing the loop from insight directly to outcome.

How are knowledge graphs and semantic data models central to your platform’s success?

Knowledge graphs and semantic data models are the absolute core of the Easy Answers platform; they elevate it beyond traditional BI tools that often treat data as disconnected tables and columns devoid of real-world business context. Our platform uses them to build an intelligent semantic layer over enterprise data.

This semantic foundation is central to our success for several key reasons:

Enables True Natural Language Interaction: The semantic model, structured as a knowledge graph with Managed Semantic Objects (MSOs), properties, and defined relationships, acts as a ‘Rosetta Stone’. It translates the nuances of human language and business terminology into the precise queries needed to retrieve data, allowing users to ask questions naturally without knowing underlying schemas. This is key to our high text-to-SQL accuracy.

Preserves Critical Business Context: Unlike simple relational joins, our knowledge graph explicitly captures the rich, complex web of relationships between business entities (e.g., how customers interact with products through support tickets and purchase orders). This allows for deeper, more contextual analysis reflecting how the business operates.

Provides Adaptability and Scalability: Semantic models are more flexible than rigid schemas. As business needs evolve or new data sources are added, the knowledge graph can be extended and modified incrementally without requiring a complete overhaul, maintaining consistency while adapting to change.

This deep understanding of data context provided by our semantic layer is fundamental to everything Easy Answers does, from basic Q&A to advanced pattern detection with Quick Insights, and it forms the essential foundation for our future agentic AI capabilities, ensuring agents can reason over data meaningfully.

What foundational models do you support, and how do you allow organizations to bring their own AI/ML models into the workflow?

We believe in an open and flexible approach, recognizing the rapid evolution of AI and respecting organizations’ existing investments.

For foundational models, we maintain integrations with leading options from multiple providers, including Google’s Gemini family, OpenAI’s GPT models, and prominent open-source alternatives like Llama. This allows organizations to choose models that best fit their performance, cost, governance, or specific capability needs. These models power various platform features, including natural language understanding for queries, SQL generation, insight summarization, and metadata generation.

Beyond these, we provide robust pathways for organizations to bring their own custom AI/ML models into the Easy Answers workflow:

Models developed in Python can often be integrated directly via our AI Engine.

We offer seamless integration capabilities with major cloud ML platforms such as Google Vertex AI and Amazon SageMaker, allowing models trained and hosted there to be invoked.

Critically, our semantic layer plays a key role in making these potentially complex custom models accessible. By linking model inputs and outputs to the business concepts defined in our knowledge graph (MSOs and properties), we allow non-technical business users to leverage advanced predictive, classification or causal models (e.g., through Quick Insights) without needing to understand the underlying data science – they interact with familiar business terms, and the platform handles the technical translation. This truly democratizes access to sophisticated AI/ML capabilities.

Looking ahead, what trends do you see shaping the next wave of enterprise AI—particularly in agent marketplaces and no-code agent design?

The next wave of enterprise AI is moving towards highly dynamic, composable, and collaborative ecosystems. Several converging trends are driving this:

Agent Marketplaces and Registries: We’ll see a significant rise in agent marketplaces functioning alongside internal agent registries. This facilitates a shift from monolithic builds to a ‘rent and compose’ model, where organizations can dynamically discover and integrate specialized agents—internal or external—with specific capabilities as needed, dramatically accelerating solution deployment.

Standardized Agent Communication: For these ecosystems to function, agents need common languages. Standardized agent-to-agent communication protocols, such as MCP (Model Context Protocol), which we leverage, and initiatives like Google’s Agent2Agent protocol, are becoming essential for enabling seamless collaboration, context sharing, and task delegation between agents, regardless of who built them or where they run.

Dynamic Orchestration: Static, pre-defined workflows will give way to dynamic orchestration. Intelligent orchestration layers will select, configure, and coordinate agents at runtime based on the specific problem context, leading to far more adaptable and resilient systems.

No-Code/Low-Code Agent Design: Democratization will extend to agent creation. No-code and low-code platforms will empower business experts, not just AI specialists, to design and build agents that encapsulate specific domain knowledge and business logic, further enriching the pool of available specialized capabilities.

App Orchid’s role is providing the critical semantic foundation for this future. For agents in these dynamic ecosystems to collaborate effectively and perform meaningful tasks, they need to understand the enterprise data. Our knowledge graph and semantic layer provide exactly that contextual understanding, enabling agents to reason and act upon data in relevant business terms.

How do you envision the role of the CTO evolving in a future where decision intelligence is democratized through agentic AI?

The democratization of decision intelligence via agentic AI fundamentally elevates the role of the CTO. It shifts from being primarily a steward of technology infrastructure to becoming a strategic orchestrator of organizational intelligence.

Key evolutions include:

From Systems Manager to Ecosystem Architect: The focus moves beyond managing siloed applications to designing, curating, and governing dynamic ecosystems of interacting agents, data sources, and analytical capabilities. This involves leveraging agent marketplaces and registries effectively.

Data Strategy as Core Business Strategy: Ensuring data is not just available but semantically rich, reliable, and accessible becomes paramount. The CTO will be central in building the knowledge graph foundation that powers intelligent systems across the enterprise.

Evolving Governance Paradigms: New governance models will be needed for agentic AI – addressing agent trust, security, ethical AI use, auditability of automated decisions, and managing emergent behaviors within agent collaborations.

Championing Adaptability: The CTO will be crucial in embedding adaptability into the organization’s technical and operational fabric, creating environments where AI-driven insights lead to rapid responses and continuous learning.

Fostering Human-AI Collaboration: A key aspect will be cultivating a culture and designing systems where humans and AI agents work synergistically, augmenting each other’s strengths.

Ultimately, the CTO becomes less about managing IT costs and more about maximizing the organization’s ‘intelligence potential’. It’s a shift towards being a true strategic partner, enabling the entire business to operate more intelligently and adaptively in an increasingly complex world.

Thank you for the great interview, readers who wish to learn more should visit App Orchid.

[paper-mario-wiki]

Hey I know your clown days are behind you and everything, but I'm still curious if you've got any opinions on The Amazing Digital Circus?

same thing everyone else does i suppose, that its fun and well made.

to be additive to the discussion instead of just saying positive stuff you've already heard, i'll levy a little critique against it, bearing in mind that i do so with positive, constructive intent.

i feel as though in recent times we've been oversaturated with stories and media with too grand a focus on the characters instead of interesting concepts. i think that the character design in the amazing digital circus is colorful and neat, but in the past 3 years how many "it's cutesy looking, but it's actually about existential terror and the cute characters go through trauma, oh no!" gimmicks have you seen in stories? personally ive seen quite a few.

i feel as though creatives are pushed too much to make a marketable face first and foremost, because lingering eyes are in high demand for artwork online these days. if someone is going to move on quick, which is going to be the case with the majority of people, it's much more likely that someone will find it easier to remember a cool design for a character instead of an entire unique sequence of events. that means story-driven things are either made to be more shallow to give more space for character moments, or have a much smaller chance for having their work succeed.

i do not think this is indicative of some mass decline in creative originality, or even the fault of the creatives who make the work. like i said, i thought it was a lot of fun and gave it a small positive review on twitter a few days ago. Gooseworx, as well as the rest of the team who made it, clearly cared for this project. what im talking about is a symptom of the larger issue that is the black-box algorithms that have desolated social networks, and create bad media consumption habits in people. though i wont lie to you and say i havent grown to resent the symptom too.

so to answer your question more succinctly, i dont really think of it that much at all. ive seen this one already.

[reasonsforhope]

"Is social media designed to reward people for acting badly?

The answer is clearly yes, given that the reward structure on social media platforms relies on popularity, as indicated by the number of responses – likes and comments – a post receives from other users. Black-box algorithms then further amplify the spread of posts that have attracted attention.

Sharing widely read content, by itself, isn’t a problem. But it becomes a problem when attention-getting, controversial content is prioritized by design. Given the design of social media sites, users form habits to automatically share the most engaging information regardless of its accuracy and potential harm. Offensive statements, attacks on out groups and false news are amplified, and misinformation often spreads further and faster than the truth.

We are two social psychologists and a marketing scholar. Our research, presented at the 2023 Nobel Prize Summit, shows that social media actually has the ability to create user habits to share high-quality content. After a few tweaks to the reward structure of social media platforms, users begin to share information that is accurate and fact-based...

Re-targeting rewards

To investigate the effect of a new reward structure, we gave financial rewards to some users for sharing accurate content and not sharing misinformation. These financial rewards simulated the positive social feedback, such as likes, that users typically receive when they share content on platforms. In essence, we created a new reward structure based on accuracy instead of attention.

As on popular social media platforms, participants in our research learned what got rewarded by sharing information and observing the outcome, without being explicitly informed of the rewards beforehand. This means that the intervention did not change the users’ goals, just their online experiences. After the change in reward structure, participants shared significantly more content that was accurate. More remarkably, users continued to share accurate content even after we removed rewards for accuracy in a subsequent round of testing. These results show that users can be given incentives to share accurate information as a matter of habit.

A different group of users received rewards for sharing misinformation and for not sharing accurate content. Surprisingly, their sharing most resembled that of users who shared news as they normally would, without any financial reward. The striking similarity between these groups reveals that social media platforms encourage users to share attention-getting content that engages others at the expense of accuracy and safety...

Doing right and doing well

Our approach, using the existing rewards on social media to create incentives for accuracy, tackles misinformation spread without significantly disrupting the sites’ business model. This has the additional advantage of altering rewards instead of introducing content restrictions, which are often controversial and costly in financial and human terms.

Implementing our proposed reward system for news sharing carries minimal costs and can be easily integrated into existing platforms. The key idea is to provide users with rewards in the form of social recognition when they share accurate news content. This can be achieved by introducing response buttons to indicate trust and accuracy. By incorporating social recognition for accurate content, algorithms that amplify popular content can leverage crowdsourcing to identify and amplify truthful information.

Both sides of the political aisle now agree that social media has challenges, and our data pinpoints the root of the problem: the design of social media platforms."

And here's the video of one of the scientsts presenting this research at the Nobel Prize Summit!

-Article via The Conversation, August 1, 2023. Video via the Nobel Prize's official Youtube channel, Nobel Prize, posted May 31, 2023.

[printedword]

For years, it was a mystery: Seemingly out of the blue, therapists would feel like they’d tripped some invisible wire and become a target of UnitedHealth Group. A company representative with the Orwellian title “care advocate” would call and grill them about why they’d seen a patient twice a week or weekly for six months. In case after case, United would refuse to cover care, leaving patients to pay out-of-pocket or go without it. The severity of their issues seemed not to matter. Around 2016, government officials began to pry open United’s black box. They found that the nation’s largest health insurance conglomerate had been using algorithms to identify providers it determined were giving too much therapy and patients it believed were receiving too much; then, the company scrutinized their cases and cut off reimbursements. By the end of 2021, United’s algorithm program had been deemed illegal in three states. But that has not stopped the company from continuing to police mental health care with arbitrary thresholds and cost-driven targets, ProPublica found, after reviewing what is effectively the company’s internal playbook for limiting and cutting therapy expenses. The insurer’s strategies are still very much alive, putting countless patients at risk of losing mental health care.

[emma23]

The bracelet affair :

Nathan bateman x reader

The quiet hum of Nathan’s estate was a comforting backdrop as you sat on the sleek kitchen counter, lazily sipping from a glass of wine. The sunlight filtered through the expansive windows, casting a soft glow over the stainless steel surfaces. Nathan stood a few feet away, effortlessly chopping vegetables, a towel slung over his shoulder. You couldn’t help but smile at the domesticity of it all—a far cry from the complex algorithms and ethical debates he usually buried himself in.

“Did you know,” you said, swirling the wine in your glass, “that normal people don’t have to code their refrigerators to remind them to buy milk?”

Nathan glanced at you, a smirk tugging at his lips. “Normal people also buy expired milk and cry about it later. I’m solving problems.”

“Uh-huh. Solving problems, or just proving to the universe you’re the smartest guy in the room?”

“Can’t it be both?” he teased, his sharp jawline flexing as he bit into a carrot slice.

This was Nathan—arrogant, self-assured, but in a way that made you laugh rather than roll your eyes. He was your opposite in many ways. Where he thrived on chaos and control, you found comfort in simplicity. But somehow, the two of you worked. You didn’t question it much anymore; you were just... together.

“Dinner will be ready in fifteen,” he said, wiping his hands. “You want to pick a playlist, or are we going full jazz night again?”

“Jazz is classic. You can’t go wrong,” you replied, hopping off the counter to browse his ridiculous music library.

“Jazz it is,” he said, watching you with an unreadable expression as you leaned over the tablet.

Dinner was, as always, delicious. Nathan had a talent for throwing together meals that seemed effortless but were undeniably gourmet. You picked at your plate, catching him watching you from the corner of his eye.

“What?” you asked, narrowing your gaze.

“Nothing,” he said quickly, too quickly.

“Bull. Spill it.”

Nathan sighed, leaning back in his chair. He reached into his pocket and pulled out a small black box. Your stomach did a small flip.

“Relax,” he said, noticing your wide-eyed look. “It’s not that.”

You huffed. “Maybe don’t pull out a jewelry box like that if you don’t want me jumping to conclusions.”

He slid the box across the table. “Open it.”

You raised an eyebrow but did as he said, revealing a sleek, gold bracelet with intricate engravings. It was undeniably beautiful, but also... not very you.

“It’s beautiful, but you don’t have to buy me jewelry,” you said. “You know I rarely wear it. I haven’t even had the opportunity to wear that ridiculously exuberant bracelet you bought.”

Nathan stiffened beside you, his casual demeanor slipping just a fraction. “I need you to wear this one,” he said.

“At all times,” he finished, his voice firm in a way that made you pause.

“Okay,” you said slowly, shrugging as you opened the clasp to put it on.

“It has a GPS chip inside,” he added, and your head snapped up.

“Wait, what?”

“Before you freak out—”

“You put a GPS in this thing?” you interrupted, glaring at him.

“It’s for your safety,” he said, holding up his hands in mock surrender. “What if something happens to you? This way, I can always find you.”

“Oh, that’s normal,” you said, dripping with sarcasm. “Totally casual to microchip your girlfriend like she’s a golden retriever.”

Nathan sighed, running a hand through his dark hair. “It’s not like that. I just... I worry about you, okay? You make bad decisions sometimes.”

Your mouth dropped open in indignation. “Excuse me?”

“Remember the time you decided to take a shortcut through that abandoned construction site at night?”

You groaned. “I told you, I thought it was faster!”

“Or the time you tried to fix the garbage disposal and almost lost a finger?”

“I didn’t know it was on!”

Nathan leaned forward, a mischievous glint in his eye. “Or the time—”

“Okay, I get it!” you cut him off, glaring. “I’m not exactly the poster child for sound decision-making. But this is still insane, Nathan.”

He leaned back, his expression softening. “Maybe. But I can’t help it. I need to know you’re safe.”

You sighed, your irritation melting slightly under the sincerity in his tone. “You know, there are less creepy ways to show you care.”

“Yeah, but this one is efficient,” he said, grinning.

You rolled your eyes but couldn’t help the small smile tugging at your lips. “Fine. I’ll wear it. But if you start tracking my every move, I’m throwing it in the ocean.”

“Deal,” he said, his grin widening.

Later that night, tangled in the sheets, you couldn’t help but laugh as Nathan’s fingers traced absentminded patterns on your skin.

“What’s so funny?” he asked, his voice low and lazy.

“Just thinking about how I’m officially a GPS-tracked girlfriend now,” you said, smirking.

Nathan chuckled, pulling you closer. “You’re not just tracked. You’re my whole world, Y/n.”

Your heart fluttered at his words, but you couldn’t resist teasing him. “Careful, Bateman. You’re starting to sound romantic.”

“Don’t get used to it,” he muttered, pressing a kiss to your shoulder.

You grinned, turning to face him. “You know, for a genius, you’re really bad at pretending you’re not a softie.”

Nathan smirked, his eyes glinting with mischief. “And you, for someone who makes terrible decisions, somehow made the right one when you picked me.”

You snorted, poking his chest. “Or maybe you picked me. Which is really the only bad decision you’ve ever made.”

He laughed, pulling you on top of him. “Guess we’re both doomed, then.”

As you leaned down to kiss him, he added, “But at least I’ll always know where you are.”

You groaned, smacking his chest. “Nathan!”

“Just saying,” he said with a wink.

[musings-of-a-rose]

All of You

Pairing: Javier Peña x f!reader (eventual wife reader)

Word Count: 2900+

Rating: Mature - 18+ ONLY!

Warnings: Just like ao3, “creator chooses not to use warnings.” If you click Keep Reading, that means you agree that you’re the age to handle mature themes. Also by clicking Keep Reading, you understand warnings may not be complete in order to avoid spoilers for the story. 

Notes: I’m not sure who originally said it, but the wonderful @morallyinept shared this and I had to write it for her! A Boxing Day gift? Is that a thing (said in American)?  Shoutout to @rhoorl for the nickname! This is not beta’d because I’m tired lol

Yeah... I'm not okay. I read a reblog comment which made me chuckle saying this is older, retired Peña who's being slowly overfed by his wife

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**Reader is not described

Main Masterlist

Javier Peña Masterlist

“I’ll be right with you!” I yell over my shoulder as the entry bell dings, boots casually walking across the hard floors of my little corner store bakery. 

I slide the baking pan in my old oven, an antique to most but she works better than most of these modern ones. I set my timer and place it on the counter next to the oven, wiping my hands on my apron as I spin around to address the customer and am momentarily rendered speechless. A man casually peruses my glass display case, all dark hair and dark eyes, a slim frame but the broadest shoulders I’ve seen. His nose is prominent, a mustache that sort of reminds me of Burt Reynolds is neatly trimmed, and he leans down to look closer at something in the case. 

Sexy would not begin to describe this man. 

“Are those coyotas?”

I blink, his voice runs through my brain and makes my body shiver, goosebumps erupting across my arms. 

“Y-yeah. Yes. Coyotas.”

He looks up at me, his eyes wide and round just like a damn puppy and I could get lost in those eyes. 

“Could I have a few?”

“Absolutely. Anything else?”

He finally looks at me, pulling his eyes away from whatever memory the coyotas held and blinks, his eyes scanning down my body, the tingles from before starting back up. 

“S-sir?”

“Huh? Oh. Uh yeah. I’m picking up an order for Chucho? Peña?”

I chuckle. “Chucho. My favorite customer! I have his order right here.” I move to grab a small bag with various pastries inside, making him a to-go cup of cafe con leche to accompany it. 

“Would you like a cup?”

“What? Oh I don’t want to put you out.”

“You’re not. How do you like it?”

“Plain?”

I pour him a black cup of coffee, sliding the lid over it before turning to hand it to him, his fingers brushing against mine as he takes it and I feel my cheeks heat up at the spark that passes between us. 

“Chucho normally comes to say hi.”

“Yeah he’s dealing with farm shit right now. Asked me to come.”

I nod. “And you are?”

“Oh shit! Sorry! I’m Javier. Chucho’s son.” He extends a hand as I say my name but does it too quickly, coffee spilling out of the cup that he had squeezed a little harder than he should have. “Fuck I’m so sorry. Let me help-”

I wave my hand. “I got it. Are you ok? Some of that got on you. Hold still.” I take a clean cloth from my apron pocket and run some water on it, turning back to Javier. I gently take his hand, placing it in mine, trying to ignore the heat that immediately sprung up between my thighs. I dab at his hand, hearing his breath come in short bursts. 

“Am I hurting you?”

“N-no. Not..hurting.”

He looks into my eyes, his pinched together and round and we just stare at each other for several moments, getting lost in the other. Then the bell rings and the spell is broken, Javier jerking his hand back as a woman walks in and I wave to her, letting her know I’d be right over. I grab Chucho’s order and coffee, carefully handing the latter to Javier.

“Wait. I haven’t paid.”

I wave him off. “Don’t worry about it. I got it.”

“No. You deserve payment.”

“Javier, really. It’s ok.” My body braver than I am, I place my hand on his forearm, giving it a little squeeze, offering him a smile. Javier shifts from foot to foot before looking at me and nodding. 

“If you insist.” He hesitates, opening his mouth to say something else but then the door bell jingles again and he closes his mouth, holding up the bag slightly in thanks. 

Javier comes to get his dad’s order every day for the next few months. I’m fairly certain Chucho will have gained some major weight by now, with all the cookies and pastries Javier brings him. But I am not complaining - any chance to see this man, hear him talk. He doesn’t tell me much about the last few years, but I imagine he can’t, not really. His job has so much confidentiality involved but it’s deeper than that. I can see it in his eyes, the hardness, sadness, regret for things he must have had to do to take down an evil man. 

So he asks me about me, where I’m from, how did I get so good at baking, all of it. I tell him how my “abuela” taught me the from moment I could talk, teaching me all the traditions that accompany each pastry. Even though we weren’t blood related, she had been really close with my mom, who reminded her of a daughter she’d lost. Javier listens with rapt attention, asking me questions to learn more as he sips his coffee. 

But one day he doesn’t come in at his normal time in the morning. Instead, Chucho walks in, smiling and giving me a quick hug before making his usual order. 

“No Javier today?” I ask, trying to be nonchalant. Which I guess I’m not because he smirks. 

“Actually, I had business in town today. Javi is mending some things in the barn for me.”

The image of a sweaty Javier fills my mind and I shake my head a little. Focus. 

“Oh. Sounds like hard work.”

There’s that smirk again. “It is. Hey, could you do me a favor? I owed him dinner and I won’t be home in time for that. Poker night at Robert’s house. If I call Rita’s, could you bring it to him?”

“I..me?”

“You close early enough?”

I’d close right now if it meant seeing sweaty Javier pounding nails.

“Y-yeah. I can do that for you.”

He smiles, handing me money for his coffee. “I’ll call Rita’s. Could you get it around 4?”

I pick up his food at Rita’s, ready and waiting for me at 4pm, and follow the directions Chucho had given me out to the Peña farm. I’d be lying to myself if I said I wasn’t nervous, getting to see Javier outside of the walls of my little bakery was something I’d only dreamed of. I figured if he were interested, he would’ve asked me out or something by now. Right?

Taking one last glance in the mirror to adjust my hair, I step out of my car, walking around to open the passenger door and grab the food, his drink secured in my other hand. I hesitate at the front door, mostly because I’m trying not to chicken out but also because my hands are full and my brain is not operating fully. I eventually decide to set his drink down on the arm of the porch chair and knock, waiting several moments. Only, no one comes and the house is quiet. I knock again, wait again, and still nothing. But then I hear a faint clink! Clink! Coming from around back where the barn is and I assume Javier is in there. 

Grabbing up the drink, I take a deep breath and head towards the barn, where I hear some more banging and a couple of swear words. Nervously, I raise my hand to the wood door and knock, despite the door already being open. The pounding stops immediately and then he walks into my vision, Javier, sweaty, no shirt, jeans with some wear on them, and a tool belt slung low on his hips. He’s wiping his hands on a handkerchief as he walks towards me, head cocked to the side but his eyes wide and…nervous?

“Pastelito?” 

I smile, clumsily holding up the food and drink. “Chucho said he was going to Robert’s and wouldn’t be home to get you the dinner he owed you.” Don’t look at his chest, don’t look at his chest. Don’t. Look. 

His eyebrows pinch together in confusion. “Owed me? He doesn’t owe me anything.”

“O-oh. I..he just asked me and I said I’d help. But you look busy, I can take this back if you don’t-”

“No!” He steps closer to me, reaching for the food. “I mean, no. I’ll…thank you, pastelito.” 

I hold out the food and drink, Javier only a couple of steps away. I finally manage to look at him and find him already looking at me, his eyes dark and bright, looking for something in mine. He takes the food, his fingers brushing against mine, only this time he doesn’t move away. His large hands pause over mine for several moments before his fingers start to trace little lines up my forearm, goosebumps pimpling my skin, my heart racing. No longer in control of my brain, my eyes scan down his shirtless chest and back up, heat flaring between my thighs. He grips my forearms, pulling me to him and I drop the food, my hands immediately coming up to touch his chest as he lifts my chin, his lips pressing against mine. Fuck, his lips are soft and he’s so warm, sweaty from his work and all I can think is how I want him to press me into this bale of hay and take me, let me take his worries away. 

One hand slides down my back, the other cradling the back of my head as his tongue pushes gently forward, my lips parting, tongue coming out to meet his. He presses his body against mine, the sweat from his chest getting me wet all over. He walks me backwards until I bump against a beam. He starts to kiss a path down my neck and I gasp, whining a little when he sucks on some spot below my ear. His hands are wandering, sliding across my body, hoisting one of my thighs up on his hip, his stomach pressing in between my thighs and I moan at the feel of it. As he reaches my boobs he stops, pulling his head up so fast I’m dizzy with the motion of it. 

“Javier?”

His eyes are nearly black, his chest heaving, and he shifts slightly where he stands. “I…I can’t.”

Ouch. “Oh. I..you can. If you need permission, you definitely have it.”

“No, it’s just-” He sighs, gently setting my leg back on the floor and stepping away from me and I feel cold despite the heat of the evening, and embarrassed.

“I’ll see you around then,” I have to get out of here before the tears come. But his hand gently closes around my arm, tugging on it lightly until I turn, swallowing hard.

“Paselito, it’s not you. Please, come sit? And I’ll explain?”

I nod, shaking my head to rid myself of the tears. At least for the moment. He sits on a bale of hay and pats the space next to him. I sit, wrapping my arms around myself for some sort of comfort. He looks at me, taking my hand in his and holy shit why are his hands so large?

“Pastelito…I..I normally rush right into the physical. Hell, that’s all I really had for the last 6 years.” He sighs. “But I don’t want to do that with you. I don’t want to rush it. I definitely want to, but I want to date you. Fuck, I sound stupid don’t I?”

“Not at all, Javier. I…I’ll assume this isn’t a line,” Javier chuckles at that. “But I would absolutely love to date you.”

We fuck at the end of the first date and through the remainder of that weekend.

10 years later…

Javier sets his utensils down, chewing the last bit of his dinner before taking a sip from his glass. “You need to stop cooking so well, mi esposa [my wife], or I may not be able to fit through the door.” He rubs at his stomach, softer and slightly more fluffy after a few years of early retirement. 

“Never. I love cooking for my husband. He’s definitely earned it.”

“Yes but soon you may not want me.” He pats his stomach and smiles, but it doesn’t reach his eyes, insecurity brimming behind it.

I set down my fork, pushing my chair back to stand up and walk over to him. His eyes follow my movements and I gesture for him to push his chair back from the table, which he does. I stand between his legs, looking down at him. I place my hand over his, where it rests on his stomach.

“You think I’d find you unattractive because of this?” He shrugs, a non committal answer. 

“Maybe. I am not in the shape I was when we met.”

“Neither am I, Javi.”

“Yes, but you’re gorgeous.”

“So are you.”

He blows air from his lips, looking away from me. Much to his surprise, I decide to straddle him, his arms quickly hooking behind my knees to help hold me. I lean forward, kissing him hard and he kisses me back, his nails digging into my skin. I’m grateful I wore a dress today, especially because there’s less layers between us. I start to move my hips, slowly at first but the heat quickly builds as I grind along his belly, breaking the kiss to gasp. He watches me, eyes wide and dark as I rub myself on this area that causes so much insecurity. 

“Fuck, Javier, you’re so fucking..ngh!” My hands grip his shoulders, digging into his skin. 

“Yes, pastelito, use me. Fuck me how you want. Show me how you feel.” His chest heaves, helping to hold me in place still, but his hands are twitching, wanting to touch me. I speed up, grinding harder and then suddenly I come, his name spilling from my lips as I leave a wet mark on his shirt. Finally, I look down at him smiling, seeing his eyes like a damn puppy. 

“I fucking love your body, Javi. All of it. I could fuck myself on all of YOU!” I scream out the last word as Javier suddenly stands, pushing me up and laying me on the table, somehow pushing dishes out of the way as he did, some of them clattering to the floor, to be picked up later. 

His hands scramble up under my dress, yanking down my soaked panties and pulling them off, groaning when he felt how wet they were. His belt buckle clanks as he undoes it and drops his pants to the floor. He lines up, but I lean up on my arm. 

“Wait.” I reach forward with my other hand and undo some of his buttons, Javi finishing the rest before yanking it off himself. I run my nails down his chest and over his belly, the damp skin there heating me up.

I meet his eyes. “You’re so fucking, hot Javi. I will never stop thinking that.”

He pushes me back down and into me at the same time and I yell his name as he splits me open, his fingers digging into my hips and pulling me towards him as he thrusts, an extra hard jut of his hips when he’s already inside, knowing how that makes me writhe and moan, my entire body like a livewire. He grunts with every thrust of his hips, baring his teeth sometimes with the force of it and all I can do is hold on, my fingers digging into his arms as I moan and yell his name. 

“Yes! Fuck me, Javi!”

His hand moves between my thighs, touching me and my legs twitch. He smirks down at me as I chant his name. “Scream my name, pastelito. Make the neighbors know who I am.”

“Ye-YES! JAVI!” I come hard, yelling his name as he asks, stars in my vision and the sound of wind rushing in my ears, but not loud enough that I don’t hear him, grunting and panting out my name as he spills into me. His forehead touches mine, his nose nuzzling into me for several moments before he sits back up with a different groan, rubbing at his back for a moment before pulling out.  

“Well my back definitely tells me I’m getting older.”

I chuckle, my breathing finally leveling out as Javi extends his hand to me, helping me sit up. He holds it, pressing a kiss to the back of my hand before placing it on his cheek, looking at me.

“So, you said you could fuck yourself on all of me?” His eyebrows are raised questioningly. 

I nod. “Oh yes.”

His eyes darken. “Then show me.”

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[ladybugmania]

To the Republican clickbaiters, rage farmers, and basement-dwelling truth benders.

Let me spell it out slowly, since nuance seems to elude you:

I am not a Democrat. And definitely not Republican.

Not a puppet like y'all, not their pawn, not anyone's programmable echo.

I don't bow to governments. Not left-wing, not right-wing, not the hollow illusion of wings on a beast that never flies. At the moment I can smell a stink that smells as bad as 1933. if you dare research it. But I don't think you can. You're all sucked in the conspiracy theory. Taken in so deep it's like the vortex of a black hole with no return.

I serve no flag but my own mind, no master but my conscience, and clearly, that terrifies you.

What's beautiful, though, truly poetic, is how my so-called “garbage posts” are haunting your timelines like a ghost you can’t exorcise.

Watching how the truth somehow crawls under your skin. The way my words, chaotic, raw, and unfiltered, poke at your fragile egos and rattle your tiny echo chambers. It’s delicious. You hate me, but you can’t look away. That’s art, baby.

So keep twisting in your outrage. Keep clutching your pearl-stained keyboards. Because if what I write disturbs your sleep, good. That means it’s working.

It means I'm creeping under your skin and getting to you. Getting what I want.

My words? They burrow in deep, don’t they?

Crawling past your talking points, nesting in your insecurities, dancing in the cobwebs of your groupthink.

You call it nonsense, yet you can’t stop reading.

You hate it, but you feed on it.

I’m not debating you, I’m dissecting you.

And you feel it.

So go ahead, keep labeling me, keep gasping like fish in your ideological fishbowl.

Just know, I’m not here to fit in your box.

I’m here to rattle it.

And clearly, mission accomplished.

With all the love your hollow souls can’t process,

– The voice under your skin,

the glitch in your algorithm,

the mind you can’t control.

Sincerely, The shadow in your algorithm.

(Not a Democrat, definitely not a Republican.

Just your worst kind of free thinker)

PS. This isn't steered to all Republicans, just the stupid dumb ones. The ones who are blinded and gagged. Taken deep into the MAGA cult. Brainwashed . The ones who bow to their masters, because that's the only way they can survive their hollow existence. Slaves to an ideology from the ORANGE idiot.