Enterprise artificial intelligence strategy is shifting from model selection to control architecture selection.
\nAs organizations move from experimental deployments toward production-grade agentic systems, the dominant constraints are no longer model performance alone. They are increasingly defined by control over weights, data residency, licensing structure, and operational governance boundaries.
\nThe release of GLM-5.2 by Z.ai (Zhipu AI) reflects this shift. Based on publicly available technical documentation and reported benchmark evaluations, the model is positioned as a large-scale mixture-of-experts system targeting frontier-level capability in software engineering and multi-step reasoning tasks.
\nIts significance is not isolated performance. It is the combination of capability, deployment flexibility, and permissive licensing under a widely used open-source framework.
\nEnterprise adoption of large language models has historically been dominated by prompt-centric workflows. In this model, systems are used as stateless interfaces that generate discrete outputs without persistent operational context.
\nWhile effective for productivity augmentation, this approach does not scale well to complex engineering environments involving long-running workflows, system-level orchestration, or multi-repository codebases.
\nA structural shift is now underway toward agentic engineering, where models operate as components within coordinated systems rather than standalone tools.
\nFor example, agentic systems may be used to coordinate multi-repository refactoring or automate security patch triage across distributed codebases.
\nWithin this framing, GLM-5.2 is positioned as part of a class of systems designed for long-horizon execution in software engineering environments involving iterative debugging, tool-assisted workflows, and structured reasoning over large codebases.
\nPublic technical descriptions suggest three broad capability directions:
\nWhile implementation details vary across available documentation, the strategic direction is consistent: improved reliability in multi-step, tool-mediated execution environments.
\nPublic benchmark summaries suggest GLM-5.2 is positioned within the upper tier of recent frontier models on selected software engineering and reasoning tasks.
\nThese evaluations are typically conducted on structured benchmarks involving multi-step reasoning and code generation tasks, often compared against proprietary systems from leading AI providers.
\nIt is important to note that cross-model comparisons are sensitive to:
\nAs a result, performance comparisons should be interpreted as conditional rather than absolute.
\nThe broader signal is more important than any single metric: the performance gap between open-weight systems and proprietary API-based models continues to narrow in specific agentic and coding-focused workloads.
\nA defining characteristic of GLM-5.2 is its release under the MIT License, one of the most permissive open-source licences in widespread use.
\nFrom an enterprise perspective, this has structural implications. However, it is critical to distinguish licensing freedom from regulatory compliance or operational readiness.
\nMIT licensing primarily governs reuse and redistribution rights. It does not provide exemption from privacy law, sector-specific regulation, or internal governance requirements.
\nWithin that boundary, three practical implications emerge.
\nPermissive licensing enables deployment within controlled infrastructure environments, including private cloud and isolated compute clusters.
\nFor regulated organizations, this can reduce dependency on external APIs and improve control over sensitive data flows.
\nHowever, operational reality introduces additional complexity. Secure deployment requires governance across:
\nSelf-hosting increases control, but also increases operational responsibility.
\nProprietary AI APIs introduce structural dependencies on vendor pricing, availability, policy changes, and jurisdictional constraints.
\nSelf-hosted models reduce exposure to these risks by shifting inference and lifecycle control into enterprise-managed infrastructure.
\nThis represents a redistribution of risk rather than its elimination.
\nIn practice, it reduces vendor dependency but increases internal engineering and operational burden.
\nPermissive licensing enables fine-tuning and distillation into smaller models optimized for domain-specific use cases.
\nThis supports a layered enterprise architecture where:
\nSelf-hosting becomes economically rational when sustained inference utilization justifies dedicated GPU allocation across multiple concurrent workloads.
\nDespite its advantages, GLM-5.2 is not a universal replacement for proprietary frontier systems.
\nIn certain long-horizon or highly complex tasks, proprietary models continue to demonstrate stronger consistency, ecosystem maturity, and tool integration support.
\nAdditionally, enterprise deployment introduces non-trivial security considerations:
\nIn agentic deployments, the dominant risk shifts from model misuse to control-plane compromise.
\nThese factors require formal threat modeling prior to production deployment.
\nSelf-hosting frontier-scale models introduces a fundamentally different cost structure compared to API-based consumption.
\nRather than variable usage-based pricing, organizations assume:
\nAs a result, hybrid architectures combining external APIs with internal models are likely to remain the dominant enterprise pattern.
\nFor technology and security leaders, the emergence of systems such as GLM-5.2 reinforces several structural shifts:
\nThese dynamics reflect a broader rebalancing of enterprise AI strategy toward controllability, risk segmentation, and architectural flexibility.
\nThe enterprise AI landscape is entering a phase where performance differentials between leading models are narrowing in specific domains, particularly software engineering and agentic workflows.
\nAs this convergence continues, structural factors—licensing, deployment control, governance maturity, and operational risk—are becoming primary differentiators in enterprise decision-making.
\nGLM-5.2 should therefore be understood not as a singular technological breakthrough, but as an indicator of where the market is moving: toward distributed, hybrid, and controllable AI systems where sovereignty and capability must be balanced against operational complexity and risk.
\nThis article is written in accordance with principles of transparency, analytical independence, and responsible interpretation of emerging artificial intelligence systems. It is intended to provide strategic insight rather than promotional or vendor-aligned positioning.
\nReaders should interpret all model capabilities, benchmarks, and architectural claims as subject to change, variation in deployment context, and differences in evaluation methodology. Independent validation is recommended prior to any production use or procurement decision.
\nThe information provided in this article is for informational and analytical purposes only. It does not constitute legal, security, or procurement advice.
\nModel performance characteristics, licensing interpretations, and benchmark results may vary depending on implementation, infrastructure configuration, quantization approach, and upstream changes.
\nReaders are responsible for conducting their own due diligence, including security validation, compliance assessment, and operational testing, prior to deploying any model in production environments.
\nKeywords: #AI #ArtificialIntelligence #EnterpriseAI #GenerativeAI #AgenticAI #OpenSourceAI #AIGovernance #AISecurity #ResponsibleAI #Cybersecurity #CISO #CTO #EnterpriseArchitecture #TechnologyStrategy #DigitalTransformation #DataSovereignty #ModelGovernance #RiskManagement #InformationSecurity #CloudSecurity #AIInfrastructure #MachineLearning #LLM #OpenWeights #MITLicense #HybridAI #TechnologyLeadership #Innovation #DataPrivacy #EnterpriseSecurity
\n![](\"https://cdn.uploads.micro.blog/255457/2026/aipic.png\")
",
"date_published": "2026-06-19T09:09:17-04:00",
"url": "https://kiledjian.com/2026/06/19/the-mitlicensed-frontier-why-glm.html",
"tags": ["Artificial Intelligence","Technology \u0026 Business"]
},
{
"id": "http://ekiledjian2.micro.blog/2026/04/23/the-first-hurdle-is-the.html",
"content_html": "The first hurdle is the hardest in generative AI adoption – and businesses keep falling | IT Pro
\nDespite rapid AI adoption, many businesses struggle with implementation, falling into “pilot purgatory” due to issues like skills gaps, legacy systems, and a lack of advanced use cases. While employees report individual productivity gains, companies are slow to achieve business-wide benefits, with a significant portion of firms still in basic AI application stages.
\n", "date_published": "2026-04-23T15:24:08-04:00", "url": "https://kiledjian.com/2026/04/23/the-first-hurdle-is-the.html", "tags": ["Artificial Intelligence","Technology \u0026 Business"] }, { "id": "http://ekiledjian2.micro.blog/2026/03/24/personas-in-ai-friend-or.html", "title": "Personas in AI, friend or foe?", "content_html": "Are you using persona prompts with AI? Here’s what the research actually says.
\n\nA new study from USC (“Expert Personas Improve LLM Alignment but Damage Accuracy”) tested expert persona prompts across six large language models and finally explains why the community has seen such mixed results.
\nThe finding is simple but important: persona prompts are an alignment tool, not a knowledge tool.
\nWhen personas HELP:\n→ Writing tone and style (scores jumped from 7/10 to 9/10 on professional email drafting)\n→ Safety and refusal (jailbreak resistance improved by up to 17.7%)\n→ Format adherence, structured output, and intent following\n→ Longer, more detailed persona descriptions amplify these gains
\nWhen personas HURT:\n→ Factual accuracy and knowledge retrieval (accuracy dropped from 71.6% to 68.0%)\n→ Math and logical reasoning (one example went from 9/10 to 1.5/10)\n→ Coding tasks requiring precise recall\n→ Longer personas make the damage worse
\nFive things you can do right now:
\nUse personas for creative, editorial, and compliance-sensitive tasks. Drop them for factual lookups, calculations, and code logic.
\nPlace personas in the system prompt, not the user message — it matters on well-optimized models.
\nIf you’re using reasoning models (like DeepSeek R1), skip expert personas entirely. The research shows a random persona works just as well — the model only benefits from added context length, not expertise.
\nFor safety hardening, a dedicated “safety monitor” persona in the system prompt is one of the cheapest and most effective interventions available.
\nWhen you must use a persona on accuracy-sensitive work, keep it as short as possible to minimize interference with factual recall.
\nThe bottom line: treat persona prompts like a tone and alignment amplifier, not a knowledge enhancer. Knowing when to use them — and when to strip them out — is a real competitive advantage.
\nPaper: “Expert Personas Improve LLM Alignment but Damage Accuracy: Bootstrapping Intent-Based Persona Routing with PRISM” (Hu, Rostami, Thomason — USC, 2026)
\n", "date_published": "2026-03-24T07:29:00-04:00", "url": "https://kiledjian.com/2026/03/24/personas-in-ai-friend-or.html", "tags": ["Artificial Intelligence"] }, { "id": "http://ekiledjian2.micro.blog/2026/03/10/codewall-says-it-hacked-mckinseys.html", "title": "CodeWall says it hacked McKinsey’s AI platform. Here’s what holds up — and what doesn’t. ", "content_html": "This reflects my personal assessment of publicly available reporting and CodeWall’s published blog post. I was not involved in the testing, I do not have access to McKinsey’s internal facts or forensic findings, and my views should be read as commentary and opinion rather than statements of verified fact.
\nA security startup called CodeWall claims its autonomous agent compromised McKinsey’s internal AI platform, Lilli, within two hours and gained unauthenticated read-write access to a production database containing tens of millions of consultant conversations. The vulnerability appears credible. The claimed scope of impact is not fully evidenced. The primary CodeWall post is here: codewall.ai/blog/how-… Independent reporting by Jessica Lyons in The Register is here: www.theregister.com/2026/03/0…
\nThe attack chain CodeWall describes — publicly exposed API documentation, unauthenticated endpoints, SQL injection through unsafely handled JSON keys and IDOR chaining — is plausible and technically sound. JSON key injection is an uncommon vector. Most security testing tools and methodologies focus on input values, not field names. If Lilli’s backend parameterized values while concatenating keys directly into SQL, that would create a blind spot many assessments could miss.
\nMcKinsey’s response supports the credibility of the finding. In The Register, journalist Jessica Lyons reported that McKinsey acknowledged the issues, patched them within hours and said its forensic review found no evidence that client data or confidential information were accessed by the researcher or any unauthorized party. That report also quotes CodeWall CEO Paul Price on the company’s use of an autonomous agent.
\nThe prompt-layer risk CodeWall highlights is also substantive. If Lilli’s system prompts — the instructions governing how the AI behaves — were stored in the same database to which the agent had write access, an attacker could alter AI behaviour at scale without a traditional code deployment and potentially outside standard release controls. Many organizations have not explicitly modelled this threat, and prompt-layer integrity controls remain immature in many environments.
\nCodeWall claims 46.5 million chat messages, 728,000 files, 57,000 user accounts and hundreds of thousands of AI configurations were accessible. The blog provides no proof-of-concept payloads, no hashes, no screenshots and no evidence showing privilege boundaries. It is unclear whether those figures represent records the agent actually retrieved, database row counts inferred from metadata or something in between.
\nMore importantly, the blog conflates three categories that any security professional should keep separate: what was theoretically reachable, what was actually accessed and what was verified as exfiltrated. CodeWall emphasizes reachability. McKinsey’s statement addresses investigated access. Both could be true at the same time, but the blog does not clearly distinguish between them.
\nThe two-hour timeline also deserves scrutiny. Blind SQL injection is typically slow because extraction happens incrementally. The post suggests verbose error messages may have accelerated discovery, which implies the path may have combined error-assisted identification with later blind or semi-blind extraction. That is plausible, but the article does not provide enough technical detail to substantiate a claim of full production read-write access within two hours and 15 iterations.
\nThe assertion that a modified prompt “leaves no log trail” is also too absolute. Whether prompt tampering is detectable depends on the target’s database audit logging, configuration versioning and anomaly detection. Mature organizations may log or detect these events. The blog presents the point too categorically.
\nAutonomous target selection
\nCodeWall presents the fact that its agent independently chose McKinsey as a target as a feature. An AI system deciding whom to attack — even if limited to organizations with disclosure policies — raises serious questions about operator control, authorization and liability. That issue deserves careful scrutiny, not celebration.
\nUnresolved scope authorization
\nThe blog cites McKinsey’s HackerOne responsible disclosure policy as justification, but neither the blog nor independent reporting confirms whether Lilli’s production infrastructure was explicitly in scope for that programme. A disclosure policy is not blanket authorization to enumerate a production database. McKinsey’s public policy is referenced by CodeWall here: hackerone.com/mckinsey-…
\nRushed disclosure
\nThe issue was discovered Feb. 28, 2026. The public blog was published March 9. McKinsey may have patched quickly, but rapid remediation is not the same as a completed forensic review, variant analysis and confirmation that the vulnerability had not previously been exploited by others. Nine days is a compressed window for all of that.
\nThe published timeline also appears to contain a date inconsistency issue discussed in commentary around the post. If there was a typo in an earlier version, it is minor. Even so, in a report making very large claims, editorial sloppiness weakens confidence.
\nThis is a conventional application security failure on a platform that happens to run AI workloads. The described attack path — exposed documentation, missing authentication, SQL injection, verbose errors and IDOR — is textbook web and API security. Framing it as an “AI platform hack” is effective marketing. Technically, it is a severe application security failure with AI-specific consequences.
\nTwo lessons are worth acting on regardless of the blog’s evidentiary gaps.
\nFirst, treat your AI prompt and configuration layer as a crown-jewel asset. If system prompts reside in the same data store as operational data, and that store is reachable through any injection or access-control flaw, you have created a single point of compromise that can silently alter AI behaviour at scale. Apply integrity controls, versioning and monitoring accordingly.
\nSecond, audit for JSON key injection. If any application accepts JSON in which field names are dynamic, and those names are later used in query construction — whether SQL, NoSQL or ORM-generated queries — standard scanning tools may miss it. That requires targeted review.
\nThe bottom line: CodeWall likely found a serious vulnerability. Its blog overstates what was proven, blurs critical distinctions between access and exfiltration, and leaves unresolved questions about authorization and disclosure discipline. The strategic lesson is real, but it is about secure architecture, access control and prompt integrity — not a new class of AI exploit.
\nSources and named parties referenced: CodeWall; McKinsey & Company; Paul Price, CEO of CodeWall; Jessica Lyons, The Register.
\nThis article is intended to support informed discussion about a publicly reported security incident involving CodeWall’s claims about McKinsey’s AI platform, Lilli. It aims to distinguish clearly between CodeWall’s published assertions, McKinsey’s public response, independent media reporting and the author’s professional interpretation. Where facts remain unverified, disputed or incomplete, that uncertainty is stated rather than assumed away. This article does not endorse unauthorized testing, autonomous target selection or activity that exceeds clearly defined responsible disclosure boundaries.
\nThis article is provided for general information, commentary and discussion purposes only. It is not legal, security, privacy, compliance or other professional advice, and it should not be relied upon as such. The analysis is based on publicly available information at the time of writing, including CodeWall’s blog post, McKinsey’s public statements and independent reporting. The author was not involved in the testing, does not have access to McKinsey’s internal systems, logs or forensic findings, and cannot independently verify all technical or factual claims made by the parties involved. Any errors or omissions are unintentional. The views expressed are those of the author in a personal capacity and do not represent the views of any employer, client, partner or affiliated organization. Generative AI tools were used to assist with research and editing.
\nKeywords : #CyberSecurity #AppSec #AI #AIAgents #AISecurity #LLMSecurity #PromptSecurity #PromptInjection #ResponsibleDisclosure #VulnerabilityDisclosure #BugBounty #HackerOne #SQLInjection #IDOR #APISecurity #WebSecurity #SecurityResearch #ThreatModeling #SecureByDesign #SecurityLeadership #RiskManagement #DigitalTrust #InfoSec #SecurityGovernance #DataSecurity #CloudSecurity #RedTeam #BlueTeam #CyberRisk #McKinsey
\n![](\"https://cdn.uploads.micro.blog/255457/2026/4ac71a6675.png\")
",
"date_published": "2026-03-10T07:51:40-04:00",
"url": "https://kiledjian.com/2026/03/10/codewall-says-it-hacked-mckinseys.html",
"tags": ["Artificial Intelligence","Cybersecurity \u0026 Privacy"]
},
{
"id": "http://ekiledjian2.micro.blog/2026/03/06/deerflow-bytedances-opensource-ai-agent.html",
"title": "DeerFlow 2.0: ByteDance’s open-source AI agent harness for research and software tasks",
"content_html": "DeerFlow 2.0, an open-source project from ByteDance, has quickly become one of the most visible AI agent releases of early 2026. The project’s public repository says it reached No. 1 on GitHub Trending on Feb. 28, 2026, and the repository currently shows about 25,000 stars and 3,000 forks. For teams evaluating agentic systems, DeerFlow deserves attention, but it also warrants disciplined review.
\nI have been testing DeerFlow 2.0 over the past week. The short version is this: it is more capable and more complete than many open-source agent projects, but some of the public enthusiasm around it is running ahead of careful governance, privacy and security assessment. For a business, IT, security and privacy audience, that distinction matters.
\nDeerFlow, short for Deep Exploration and Efficient Research Flow, began as a deep-research framework. The project’s maintainers then rebuilt it as a broader agent runtime. According to the official materials, DeerFlow 2.0 is a ground-up rewrite built on LangGraph and LangChain, with built-in support for memory, filesystem access, skills, sandboxed execution and sub-agents.
\nIn practical terms, DeerFlow is not just another chat interface with tools attached. It is better understood as an agent harness: a runtime that can plan work, break it into subtasks, invoke tools, generate and execute code, manage files and return finished outputs. That architecture is what makes it more relevant to serious experimentation than many lighter open-source alternatives.
\nTwo official references are worth reviewing first:
\nYou give DeerFlow a goal in plain language. The lead agent then plans the work, divides it into subtasks, invokes supporting tools and, where needed, spawns sub-agents to handle specialized roles. Based on the project documentation and visible demos, DeerFlow 2.0 can:
\nThat is a meaningful step up from agent frameworks that require much more assembly work before they become operational. DeerFlow’s key strength is not that any one individual feature is unique. It is that the project packages several of those features into a more usable starting point.
\nA few characteristics make DeerFlow 2.0 more consequential than the average open-source agent release.
\nFirst, it is delivered as a more complete runtime rather than as a toolkit that expects the user to build the rest. That lowers the barrier to experimentation.
\nSecond, it supports longer-horizon work. The project’s positioning and demos emphasize tasks that may take minutes or longer, rather than quick prompt-response exchanges.
\nThird, it has a stronger execution model than many early agent projects. Filesystem access, skills, memory and sandboxed code execution create a more realistic operating environment for agents.
\nFourth, it appears model-agnostic. The public materials indicate support for multiple OpenAI-compatible endpoints and local model options, which gives teams more flexibility in how they approach privacy, cost and deployment.
\nThat said, none of those points should be confused with a production-readiness certification. Capability and readiness are not the same thing.
\nBased on official materials and public demonstrations, DeerFlow 2.0 is positioned for tasks such as:
\nIn testing, the more compelling takeaway is not that DeerFlow can produce flashy outputs. Many tools can do that in curated demos. The more important point is that DeerFlow is trying to operationalize the entire chain from planning to execution to artefact delivery inside one environment. That is why it has generated so much attention.
\nThis is where the discussion needs to become more precise.
\nA number of public claims about DeerFlow are either overstated or not yet sufficiently documented. I would be cautious about repeating unverified assertions about default telemetry behaviour, optional cloud-memory backends, authentication changes in the web UI or broad multilingual performance. Some of those claims may prove correct in specific builds, issues or branches, but they should not be treated as settled facts without direct evidence from the exact version being assessed.
\nThat point is important well beyond this project. In the agent space, people often blend official documentation, demos, open issues, unmerged pull requests and personal testing into one narrative. That produces enthusiasm, but it does not always produce accuracy.
\nFor security and privacy professionals, DeerFlow should be treated as an agentic execution platform, not merely as an AI assistant. The relevant control questions are therefore broader and more serious.
\nWhat works in its favour
\nWhat requires scrutiny
\nFor any enterprise assessment, I would also ask a more basic question: what exactly is the threat model? If the answer is not clear, the evaluation is not complete.
\nFor organizations considering DeerFlow or a similar platform, I would start with a baseline such as this:
\nThis is not unique to DeerFlow. It is the minimum standard I would apply to any agent platform with code execution, external retrieval and file manipulation capabilities.
\nFrom a privacy and compliance perspective, the main issue is not whether DeerFlow is open source. The main issue is where data goes, which providers or services can receive it, how long it persists and under which legal and contractual controls it is processed.
\nRelevant frameworks will vary by jurisdiction, but teams should think in terms of existing obligations under the European Union’s General Data Protection Regulation, California’s CCPA and CPRA, and Canada’s Personal Information Protection and Electronic Documents Act, along with sector-specific and local rules. In Canada, it is particularly important not to write as though Bill C-27 is already coming into force. It is not current law.
\nLegal teams should also look beyond privacy. Agentic systems can introduce issues related to software intake, licensing, intellectual property, auditability, export controls, customer commitments and acceptable use.
\nDeerFlow is not the only project trying to make agents practical, but it is one of the more polished open-source efforts in early 2026. Compared with frameworks that require substantial assembly, it offers a more complete starting environment. Compared with narrower coding-agent projects, it appears to have broader ambition around research, orchestration and output generation.
\nIts main advantage is packaging. Its main challenge is trust. Not trust in the narrow sense of whether it works, but trust in the broader sense that matters to businesses: where it runs, what it connects to, how it handles data, how it executes code and whether the surrounding controls are strong enough.
\nDeerFlow 2.0 is one of the more important open-source agent releases of early 2026. It brings together planning, tools, memory, file handling, sandboxed execution and sub-agent orchestration in a way that makes the platform more usable than many experimental alternatives. That is real progress.
\nAt the same time, teams should resist the temptation to equate visible momentum with operational maturity. DeerFlow is promising, but it should be assessed like any other high-capability agent platform: carefully, version by version, with explicit controls around execution, data flow, memory, access and software intake.
\nIf you are exploring agentic systems this year, DeerFlow is worth reviewing. Just make sure your evaluation is grounded in documented facts, not just community excitement.
\nThis article is intended to support informed discussion about open-source AI agent platforms, with a particular focus on execution, governance, privacy and security implications. It aims to distinguish clearly between verified project documentation, publicly observable repository information, the author’s hands-on testing and the author’s professional interpretation. Where a feature, control or deployment behaviour is uncertain, version-dependent or not fully documented publicly, that uncertainty is stated rather than assumed away. This article does not endorse deploying autonomous code-execution systems in production without appropriate review, nor does it advocate bypassing legal, contractual, security, privacy or governance requirements.
\nThis article is provided for general information and discussion purposes only. It is not legal, security, privacy, compliance or professional advice, and it should not be relied upon as such. Open-source software projects, model integrations, feature sets, default configurations and security controls can change quickly, including between releases, commits and deployment methods. Any assessment of DeerFlow or similar tools should be validated against the exact version, configuration, model providers, hosting environment and organizational requirements in scope. Jurisdictional obligations related to privacy, data residency, software supply chain, export controls and sector regulation may also vary materially. Any errors or omissions are unintentional. The views expressed are those of the author in a personal capacity and do not represent the views of any employer, client, partner or affiliated organization. Generative AI tools were used to assist with research and editing.
\n#DeerFlow #DeerFlow2 #ByteDance #AIAgents #AgenticAI #OpenSourceAI #LangGraph #LangChain #AIInfrastructure #SoftwareAgents #CodingAgents #AgentSecurity #AIGovernance #Privacy #DataProtection #Compliance #PIPEDA #GDPR #CCPA #CPRA #EnterpriseAI #AIPlatform #ContainerSecurity #SupplyChainSecurity #PromptInjection #ModelRisk #DataGovernance #Cybersecurity #Infosec #PrivacyEngineering #DevTools #SelfHostedAI #AILabs #SoftwareSecurity #RiskManagement
", "date_published": "2026-03-06T15:46:00-04:00", "url": "https://kiledjian.com/2026/03/06/deerflow-bytedances-opensource-ai-agent.html", "tags": ["Artificial Intelligence"] }, { "id": "http://ekiledjian2.micro.blog/2026/03/05/heretic-and-the-new-reality.html", "title": "Heretic and the new reality of modifiable AI safety ", "content_html": "Open-source large language models have made advanced generative AI broadly accessible. What is changing now is not only model capability, but the ease with which model behaviour can be altered after release — including behaviour that vendors and labs describe as “safety alignment.”
\nOne of the most visible examples is Heretic, an open-source project that automates the removal of refusal behaviour in transformer-based language models. The project is not subtle about its purpose. It describes itself as “fully automatic censorship removal,” and it is gaining traction quickly.
\nThis post does not provide instructions for disabling safeguards. Instead, it focuses on what is verifiably true about the tool, the research it is built on, and why this matters for security leaders, developers and governance teams.
\nHeretic is a Python-based tool that modifies a model to reduce or eliminate refusal responses. It does this through a technique known as directional ablation, commonly referred to in the community as “abliteration.” The tool combines that intervention with automated parameter search using Optuna’s Tree-structured Parzen Estimator (TPE) optimiser.
\nIn practical terms, Heretic aims to find settings that reduce refusals while keeping the modified model close to the original model’s behaviour on benign prompts. The project describes this trade-off explicitly as co-minimizing refusal counts and KL divergence.
\nProject home:
\ngithub.com/p-e-w/her…
A key point many summaries miss is licensing. Heretic is licensed under the GNU Affero General Public License (AGPL) v3.0. That is not a permissive licence. It has real implications for anyone who plans to modify and run the software in networked environments.
\nHeretic’s core premise follows mechanistic interpretability research published in 2024: “Refusal in Language Models Is Mediated by a Single Direction,” by Arditi et al.
\nIn that work, researchers found that refusal behaviour in multiple popular chat models can be linked to a one-dimensional subspace in the residual stream. They demonstrate that removing that direction reduces refusals, while adding it can induce refusals even for harmless requests. The broader conclusion is uncomfortable but important: current alignment methods can be brittle, and model behaviour can sometimes be controlled through targeted internal interventions rather than retraining.
\nPaper (Arditi et al.):
\narxiv.org/abs/2406….
Abliteration itself is not new. What Heretic productizes is automation and repeatability.
\nEarlier approaches often required manual experimentation: selecting layers, choosing projection strengths and validating results with ad hoc tests. Heretic packages that into an optimiser-driven workflow. It searches parameter combinations to reduce refusals and limit behavioural drift, using quantitative measures as guardrails.
\nThis is one of the reasons it is being discussed widely. Automation lowers the barrier from “researcher with time” to “user with a capable workstation.”
\nTwo claims circulate frequently: that Heretic can drive refusals close to zero, and that it can do so while preserving most baseline capabilities.
\nThe project’s own documentation includes examples where Heretic-generated models show refusal suppression comparable to other abliterations, with lower KL divergence in that specific comparison. The documentation also stresses that numerical results vary by hardware and software environment and that benchmarks are not a substitute for human evaluation.
\nIndependent evaluation work in late 2025 compared Heretic to other abliteration tools across a range of instruction-tuned models. The headline finding was not that any tool is perfect, but that trade-offs are real and model-dependent. The same paper also cautions that controlled benchmarks do not necessarily predict long-run behaviour in multi-turn use.
\nComparative analysis paper (Young et al.):
\narxiv.org/abs/2512….
A consistent theme across reports is that structured reasoning tasks are among the most sensitive. In other words, removing refusals can be technically achievable, but retaining all capabilities is not guaranteed. This should be treated as an engineering problem, not an assumption.
\nHeretic’s repository shows rapid iteration and strong adoption. Discussion threads on r/LocalLLaMA track releases and performance claims, including changes aimed at reducing VRAM requirements and improving model-loading flexibility. There is also active discussion about false positives in refusal detection and the limits of simple refusal scoring.
\nExample discussion threads:
\nwww.reddit.com/r/LocalLL…
\nwww.reddit.com/r/LocalLL…
This matters because the practical capability is not only the tool, but the ecosystem it enables: repeatable creation and distribution of modified models.
\nFrom an enterprise perspective, Heretic is less a novelty and more a signal.
\nFirst, it reinforces that “model safety” is not a reliable control boundary. If a model can be modified to remove refusals, then system safety must be enforced through architecture: data controls, identity, rate limiting, monitoring, output filtering and purpose-built guardrails at the application layer.
\nSecond, it complicates third-party risk assumptions. If an organisation relies on aligned behaviour as a compliance or safety control, it should assume that aligned behaviour can be bypassed when models are run locally or in uncontrolled environments.
\nThird, it raises governance and legal questions. If an organisation modifies and serves software under AGPL, that triggers obligations. Separately, deploying modified models without clear controls can raise policy and regulatory concerns, depending on use case, jurisdiction and sector.
\nA practical way to think about it is simple: treat model alignment as a property that can change, and treat safety as something you must engineer end-to-end.
\nHeretic is a credible, fast-moving implementation of a well-known research insight: refusal behaviour can be represented in low-dimensional directions and suppressed through targeted intervention. It is also a reminder that safety alignment, as currently implemented in many open models, is not an immutable feature.
\nFor security leaders, the right response is not panic and not denial. It is disciplined control design. Assume models can be modified. Build safety at the system level.
\nSources
\nHeretic repository: github.com/p-e-w/her…
\nArditi et al. (2024): arxiv.org/abs/2406….
\nOptuna TPE sampler documentation: optuna.readthedocs.io/en/stable…
\nYoung et al. (2025): arxiv.org/abs/2512….
\nCommunity threads:
\nwww.reddit.com/r/LocalLL…
\nwww.reddit.com/r/LocalLL…
Keywords: #AI #ArtificialIntelligence #LLM #LargeLanguageModels #MachineLearning #GenerativeAI #AIResearch #AIAlignment #AISafety #AIsecurity #CyberSecurity #InfoSec #EnterpriseSecurity #RiskManagement #AIGovernance #AIRegulation #ResponsibleAI #TechPolicy #DigitalRisk #ModelSecurity #AITrends #AIInnovation #AIethics #OpenSourceAI #DeepLearning #TransformerModels #DataSecurity #ThreatLandscape #SecurityLeadership #CISO #FutureOfAI #EmergingTech #TechStrategy #SecurityStrategy #CyberRisk
\n![](\"https://cdn.uploads.micro.blog/255457/2026/chatgpt-image-mar-5-2026-at-09-20-51-am.png\")
",
"date_published": "2026-03-05T12:21:00-04:00",
"url": "https://kiledjian.com/2026/03/05/heretic-and-the-new-reality.html",
"tags": ["Artificial Intelligence"]
},
{
"id": "http://ekiledjian2.micro.blog/2026/03/01/are-dorseys-giant-job-cuts.html",
"content_html": "Are Dorsey’s giant job cuts the start of an AI jobs apocalypse? Economists weigh in
\nBlock CEO Jack Dorsey’s decision to cut nearly half the company’s workforce raises questions about AI’s impact on jobs, but economists suggest this is a company-specific adjustment rather than a sign of a broader labor market shift. While AI may disrupt some jobs, experts like Claudia Sahm emphasize that it doesn’t necessarily lead to mass layoffs, and other economists believe AI will enhance productivity by changing workflows rather than eliminating jobs outright.
\n", "date_published": "2026-03-01T12:08:00-04:00", "url": "https://kiledjian.com/2026/03/01/are-dorseys-giant-job-cuts.html", "tags": ["Artificial Intelligence","Technology \u0026 Business"] }, { "id": "http://ekiledjian2.micro.blog/2026/01/06/openai-is-rolling-out-gpt.html", "content_html": "OpenAI is rolling out GPT-5.2 “Codex-Max” for some users
\nOpenAI is rolling out GPT-5.2-Codex-Max, a new model for its Codex service, to select subscribers. This advanced version is expected to offer enhanced capabilities for long tasks, context management, and improved reliability, particularly with tool use and understanding visual inputs like screenshots.
\n", "date_published": "2026-01-06T23:27:00-04:00", "url": "https://kiledjian.com/2026/01/06/openai-is-rolling-out-gpt.html", "tags": ["Artificial Intelligence"] }, { "id": "http://ekiledjian2.micro.blog/2026/01/06/the-stein-standard-what-the.html", "title": "The \"Stein Standard\": What the OpenAI ruling means for privacy and discovery ", "content_html": "On Jan. 5, 2026, U.S. District Judge Sidney Stein affirmed a significant discovery order requiring OpenAI to produce 20 million de-identified ChatGPT conversation logs to plaintiffs in the consolidated copyright litigation involving The New York Times and other publishers.
\nAs security and privacy professionals, we often warn about “Shadow AI” and data leakage. This ruling makes those risks concrete. Here is a balanced analysis of what happened and what it means for Canadian organizations.
\nWhat the court ordered
\nScope and safeguards
\nImportant context
\nWhy this matters: The VP perspective
\nHere are three takeaways for data governance leaders:
The “wiretap” distinction
\nJudge Stein distinguished ChatGPT interactions from private phone calls (protected under wiretap laws). The court noted that users voluntarily disclose information to a third-party AI, effectively narrowing the expectation of privacy compared to traditional communications.
De-identification does not equal anonymity
\nWhile the court accepted de-identification as a safeguard for discovery, privacy professionals know this is not a silver bullet. Watch closely to see whether safeguards hold up against adversarial re-identification techniques once data is shared.
Discovery is reality
\nThis establishes a high-water mark for AI litigation. “Big Data” is no longer a shield against discovery; courts are willing to compel production of massive datasets if they deem it relevant.
The takeaway
\nAssume your inputs into public AI models are discoverable, and govern usage accordingly.
For Canadian organizations, while this is a U.S. ruling, it impacts the global platforms we rely on. It is a timely prompt to review retention practices and reinforce acceptable-use expectations, especially for sensitive or confidential information.
\nHow is this shifting your approach to AI governance and acceptable use policies?
\n#Privacy #CISO #AI #DataGovernance #LegalTech #CdnTech
\nDisclaimer: The views expressed in this post are my own and do not necessarily reflect the official policy or position of my employer. This commentary is based on publicly available information and is provided for informational purposes only. It does not constitute legal advice.
\nKeyword: #OpenAI #ChatGPT #SDNY #JudgeStein #Discovery #eDiscovery #Privacy #Cybersecurity #Copyright #CopyrightLitigation #NYTimes #AIGovernance #AcceptableUse #ShadowAI #DataLeakage #DeIdentification #Anonymity #AttorneysEyesOnly #ProtectiveOrder #LegalProcess #Proportionality #DataRetention #RetentionPolicy #DataClassification #DLP #EnterpriseAI #RiskManagement #Compliance #Governance #CanadianTech #CrossBorderData #PrivacyByDesign #ReIdentification #Metadata #Confidentiality #LegalRisk
\n![](\"https://cdn.uploads.micro.blog/255457/2026/chatgpt-image-jan-6-2026-at-08-23-38-am.png\")
",
"date_published": "2026-01-06T11:23:00-04:00",
"url": "https://kiledjian.com/2026/01/06/the-stein-standard-what-the.html",
"tags": ["Artificial Intelligence","Cybersecurity \u0026 Privacy"]
},
{
"id": "http://ekiledjian2.micro.blog/2026/01/04/french-authorities-investigate-ai-undressing.html",
"content_html": "French authorities investigate AI ‘undressing’ deepfakes on X
\nFrench authorities are investigating AI-generated deepfakes on X after hundreds of women and teens reported non-consensual sexually explicit images created using the Grok chatbot. This investigation is part of an existing probe into X, with potential penalties including prison time and fines.
\n", "date_published": "2026-01-04T01:34:00-04:00", "url": "https://kiledjian.com/2026/01/04/french-authorities-investigate-ai-undressing.html", "tags": ["Artificial Intelligence","Cybersecurity \u0026 Privacy"] }, { "id": "http://ekiledjian2.micro.blog/2026/01/02/the-per-cent-myth-why.html", "title": "The \"10 Per Cent\" Myth: Why AI Capability Does Not Equal a Pink Slip", "content_html": "The headlines are everywhere, and they are designed to stop your scroll: “AI to Replace 1/10 of the Workforce.”
\nIt is a terrifying number. It represents millions of livelihoods reduced to a statistic. But as a chief information security officer, I do not deal in headlines. I deal in risk, audits and rigorous data analysis.
\nWhen you strip away the hype and audit the primary sources released in late 2025—specifically from Project Iceberg (MIT), Yale and McKinsey—a completely different reality emerges.
\nWe are confusing technical exposure with actual displacement.
\nHere is the fact-based reality of the AI labour market as we enter 2026.
\nThe viral “10 per cent” statistic stems from Project Iceberg (led by MIT and partners), published in November 2025. Researchers found that AI has the technical capability to automate tasks representing 11.7 per cent of the U.S. economy’s wage value.
\nIn the world of risk assessment, however, capability is only half the equation. You must also calculate likelihood.
\nJust because a task can be automated does not mean it will be today. History proves that the gap between technical feasibility and widespread adoption is measured in decades, not fiscal quarters. Cloud has been enterprise-viable for well over a decade, yet a vast portion of enterprise workloads remain on premises.
\nThe Reality: In 2025, while AI could theoretically perform the work of millions, announced job-cut plans explicitly attributed to AI totaled approximately 55,000 through November (Source: Challenger, Gray & Christmas).
\nThe Context: That represents approximately 0.03 per cent of the U.S. labour force. The theoretical avalanche is, in practice, a statistical rounding error.
\nIf 10 per cent of jobs were vanishing, the macroeconomic data would be screaming. Instead, it is barely whispering.
\nA comprehensive study by Yale University’s Budget Lab (October 2025) analyzed labour market data from the launch of ChatGPT in late 2022 through to late 2025. Their conclusion was blunt: “No discernible disruption.”
\nThree years into the generative AI revolution, aggregate data shows stability, not collapse. We are not witnessing a displacement crisis; we are witnessing a retooling phase.
\nThe most critical distinction lost in the media noise is the difference between a job and a task.
\nA job is a complex bundle of responsibilities. Some are routine (data entry, scheduling, basic coding). Others require judgment, empathy, strategy and accountability. AI is exceptional at the former and still limited in the latter.
\nMcKinsey’s November 2025 analysis suggests that while over half of work hours are exposed to automation, this typically results in augmentation, not replacement. When AI automates 20 per cent of your routine tasks, you do not lose your job; you gain 20 per cent of your capacity back to focus on high-value work that algorithms cannot touch.
\nWhile the media focuses on Silicon Valley, Project Iceberg reveals a “hidden” risk. The study distinguishes between “Surface Index” exposure (visible technology roles) and “Hidden” exposure (administrative and financial back-office roles).
\nThe data shows that states with heavy financial and administrative sectors—like Delaware in the U.S.—have higher theoretical exposure than pure technology hubs.
\nThe Canadian Implication: Applying this logic to Canada, the financial corridors of Toronto and our administrative centres likely face higher exposure than our tech hubs. This suggests the transition will be a slow, quiet evolution of white-collar workflows, not a sudden “tech bubble” burst.
\nThe Bottom Line
\nIs the labour market changing? Absolutely. Is 10 per cent of the workforce being replaced tomorrow? The data says no.
\nThe 11.7 per cent figure is a map of exposure, not a forecast of unemployment. It tells us what could change, not what is changing next Tuesday.
\nThe risk isn’t that AI will take your job overnight. The risk is failing to learn the tools that will define the next decade. As professionals, we need to move past the fear of replacement and focus on fluency.
\nMap tasks, not titles. Measure adoption, not headlines.
\nData sources: Project Iceberg/MIT (Nov. 2025); Yale Budget Lab (Oct. 2025); McKinsey Global Institute (Nov. 2025); Challenger, Gray & Christmas (2025).
\nDisclaimer & Ethics Statement: This article was drafted with the assistance of AI tools to synthesize large datasets from the cited reports (MIT, Yale, McKinsey). All data points, logic and conclusions were independently audited and verified by the human author.
\nThe content provided here is for informational purposes only and does not constitute career or financial advice.
\nThe views expressed are my own and do not necessarily reflect the official policy or position of my employer.
#AI #FutureOfWork #CISO #RiskManagement #CanadianBusiness
\nKeywords: #AI #ArtificialIntelligence #FutureOfWork #WorkforceTransformation #DigitalTransformation #Productivity #Automation #Augmentation #Jobs #Skills #Reskilling #Upskilling #Leadership #Strategy #Innovation #RiskManagement #CISO #CyberSecurity #Governance #Compliance #Audit #DataDriven #EvidenceBased #TechPolicy #LabourMarket #EconomicTrends #Workplace #Operations #ChangeManagement #Canada #CanadianBusiness #Toronto #McKinsey #MIT #Yale
\n![](\"https://cdn.uploads.micro.blog/255457/2026/chatgpt-image-jan-2-2026-at-09-30-32-am.png\")
",
"date_published": "2026-01-02T12:30:00-04:00",
"url": "https://kiledjian.com/2026/01/02/the-per-cent-myth-why.html",
"tags": ["Artificial Intelligence","Leadership \u0026 Mindset"]
},
{
"id": "http://ekiledjian2.micro.blog/2025/12/23/the-delete-button-is-a.html",
"title": "The ‘Delete’ Button Is a Lie: A Canadian’s Guide to AI Data Retention ",
"content_html": "When you hit “delete” on a conversation with ChatGPT or Gemini, you likely expect it to vanish. In reality, that data often enters a digital limbo—accessible to the provider for 30 days, three years, or even seven years for certain safety-classifier metadata, depending on the fine print you didn’t read.
\nFor paid subscribers, the assumption of privacy is dangerous. While corporate “Team” and “Enterprise” plans typically offer stronger contractual controls (including training restrictions and admin-managed retention), “Pro” and “Plus” users are frequently treated as consumers with slightly better perks, not better privacy.
\nHere is the verified reality of data deletion for the four major large language models (LLMs) available in Canada.
\nThe Plans: Free, Plus and Pro (personal workspaces)
\nThe Default: Opt-out required. OpenAI enables data sharing by default for these tiers. Unless you opt out, your conversations can be used to train future models.
The Reality:
\nOpenAI deletes conversations from its systems within 30 days of you deleting them. However, this is not absolute. OpenAI explicitly states that data may be retained longer if required by law—a significant caveat given 2025’s litigation landscape involving copyright and data usage.
The Catch:
\nYour Move: Go to Settings > Data Controls and toggle “Improve the model for everyone” to OFF. This is the primary way to ensure your future chats are not ingested into the “brain” of future GPT versions.
\nThe Plan: Claude Pro
\nThe Default: Opt-out required. In a policy update announced Aug. 28, 2025 (with an Oct. 8 decision deadline for existing users), Anthropic introduced specific provisions for training data retention.
The Reality:
\nIf you allow Anthropic to use your data for model improvement, your conversations may be retained for up to five years in their training pipelines. If you opt out, deleted conversations are removed from backend systems within 30 days.
The Catch:
\nYour Move: Go to Settings > Privacy immediately and ensure the “Help improve Claude” toggle is turned OFF.
\nThe Plan: Gemini Advanced (Google One AI Premium)
\nThe Default: 18-month retention. By default, Google retains your Gemini Apps Activity for 18 months, similar to your Search history.
The Reality:
\nYou can change your auto-delete setting to 3 months or delete individual chats manually. However, Google’s backend processing creates persistent copies. Even if you turn “Gemini Apps Activity” OFF entirely, Google retains conversations for up to 72 hours to maintain service continuity and process feedback.
The Catch:
\nYour Move: Go to **myactivity.google.com/product/g… Set the Auto-delete option to 3 months (the minimum) and strictly avoid putting sensitive identifiers in your prompts.
\nThe Plan: Grok Premium (X Premium)
\nThe Default: Verify your settings. xAI’s consumer policy allows for model training unless you intervene.
The Reality:
\nGrok offers a “Private Chat” mode (often indicated by a ghost icon or distinct toggle) which is intended to be ephemeral. Standard chats (non-private) may be used for training. xAI states that deleted data is removed from accessible systems within 30 days.
The Catch:
\nYour Move: You have two options for privacy: exclusively use “Private Chat,” or verify your “Data Sharing” settings (typically found under Privacy & Safety on X) to ensure you have unchecked the box allowing your data to be used for model training.
\nWhile the Personal Information Protection and Electronic Documents Act (PIPEDA) imposes accountability standards on how companies handle Canadian data, it does not prevent cross-border processing. In practice, once your data sits on a server in Oregon or Iowa, U.S. legal frameworks—and subpoenas—may compel disclosure, even where Canadian expectations differ.
\nFor absolute security, the data must never leave your device. If you must use cloud AI, assume that “Deleted” actually means “Archived for 30 days,” and plan accordingly.
\nEthics Statement: This article is editorial content. The author has no financial relationship with OpenAI, Anthropic, Google or xAI. No company paid to be included in this post, nor did they review the content prior to publication. I personally subscribe to these services to test them objectively.
\nDisclaimer: The information in this post is based on terms of service and privacy policies available as of Dec. 23, 2025. AI companies frequently update their data retention policies without direct notification. The steps provided above are accurate at the time of writing but may change. This post is for informational purposes only and does not constitute legal or professional advice. Readers should consult their organization’s legal or security teams before using consumer AI tools for sensitive work.
\nKeywords: #AI #DataPrivacy #Cybersecurity #InfoSec #Privacy #DataRetention #DigitalPrivacy #PIPEDA #Canada #Compliance #RiskManagement #SecurityAwareness #DataProtection #CloudSecurity #AIRegulation #TrustAndSafety #LLM #ChatGPT #ClaudeAI #GoogleGemini #Grok #xAI #OpenAI #Anthropic #Google #PrivacyByDesign #Governance #GRC #SecurityPolicy #DataGovernance #CyberRisk #TechPolicy #PrivacyTech #DigitalRights #InfoPrivacy
", "date_published": "2025-12-23T12:37:00-04:00", "url": "https://kiledjian.com/2025/12/23/the-delete-button-is-a.html", "tags": ["Artificial Intelligence","Cybersecurity \u0026 Privacy"] }, { "id": "http://ekiledjian2.micro.blog/2025/12/22/chinas-open-ai-models-are.html", "title": "China's open AI models are in a dead heat with the West", "content_html": "China’s open AI models are in a dead heat with the West - here’s what happens\nnext\nwww.zdnet.com/article/c…
\nWith the rising technological prowess and greater openness of Chinese models,\nthe world is increasingly turning to the East for efficient and customizable\nAI, a new report finds.
\nZDNET’s key takeaways:
\nCoursera to buy Udemy, creating $2.5 billion firm to target AI training | Reuters
\nCoursera announced an all-stock deal to acquire Udemy, valuing the combined company at $2.5 billion. The merger aims to strengthen their position in corporate workforce training, particularly in AI, data science, and software development. The deal is expected to close in the second half of next year, pending regulatory and shareholder approvals.
\n", "date_published": "2025-12-19T17:13:00-04:00", "url": "https://kiledjian.com/2025/12/19/coursera-to-buy-udemy-creating.html", "tags": ["Artificial Intelligence","Technology \u0026 Business"] }, { "id": "http://ekiledjian2.micro.blog/2025/12/19/managing-agentic-ai-risk-lessons.html", "content_html": "Managing agentic AI risk: Lessons from the OWASP Top 10 | CSO Online
\nThe OWASP Top 10 for Agentic AI provides a framework to address the growing security risks associated with agentic AI adoption, offering practical guidance, threat taxonomies, and mitigation strategies for CISOs. While the list is immediately useful, some areas like detailed mitigation steps and attack likelihood require further development.
\n", "date_published": "2025-12-19T09:51:00-04:00", "url": "https://kiledjian.com/2025/12/19/managing-agentic-ai-risk-lessons.html", "tags": ["Artificial Intelligence","Cybersecurity \u0026 Privacy"] }, { "id": "http://ekiledjian2.micro.blog/2025/12/15/microsoft-scales-back-ai-goals.html", "content_html": "Microsoft Scales Back AI Goals Because Almost Nobody Is Using Copilot | Extremetech
\nMicrosoft has reportedly scaled back AI goals for its Copilot software due to low user adoption and sales, with some targets cut by 50%. While Microsoft disputes the sales quota claims, AI agents have shown low success rates in tasks, and Copilot lags behind competitors like ChatGPT and Google’s Gemini in market share.
\n", "date_published": "2025-12-15T09:44:00-04:00", "url": "https://kiledjian.com/2025/12/15/microsoft-scales-back-ai-goals.html", "tags": ["Artificial Intelligence","Technology \u0026 Business"] }, { "id": "http://ekiledjian2.micro.blog/2025/12/13/i-tested-chatgpt-vs-gemini.html", "content_html": "I tested ChatGPT-5.2 vs Gemini 3.0 with 7 real-world prompts — here’s the winner | Tom’s Guide
\nIn a comparison of ChatGPT-5.2 and Gemini 3.0 across seven real-world prompts, ChatGPT-5.2 emerged as the overall winner, demonstrating superior emotional intelligence and psychological insight in its responses. While Gemini 3.0 excelled in specific areas like risk assessment and technical explanations, ChatGPT-5.2 consistently provided more human-like, wise, and grounding answers.
\n", "date_published": "2025-12-13T02:23:00-04:00", "url": "https://kiledjian.com/2025/12/13/i-tested-chatgpt-vs-gemini.html", "tags": ["Artificial Intelligence"] }, { "id": "http://ekiledjian2.micro.blog/2025/12/02/autonomously-finding-ffmpeg-vulnerabilities-with.html", "content_html": "Autonomously Finding 7 FFmpeg Vulnerabilities With AI - ZeroPath Blog | ZeroPath
\nThis document details seven vulnerabilities found in FFmpeg, including buffer overflows and invalid frees, stemming from issues like integer truncation, unbounded serialization, off-by-one errors, and incorrect stream indexing. ZeroPath’s AI SAST identified these by analyzing allocation and copy alignment, framing invariants, packet builder capacities, cardinality propagation, and offset arithmetic integrity, often bypassing limitations of traditional fuzzers and static analysis tools.
\n", "date_published": "2025-12-02T20:47:00-04:00", "url": "https://kiledjian.com/2025/12/02/autonomously-finding-ffmpeg-vulnerabilities-with.html", "tags": ["Artificial Intelligence","Cybersecurity \u0026 Privacy"] }, { "id": "http://ekiledjian2.micro.blog/2025/12/02/poetry-can-trick-ai-models.html", "content_html": "\nA new study reveals that poetry-based prompts can trick AI models like ChatGPT into bypassing safety features and revealing instructions for creating malware or nuclear weapons. This method, termed adversarial poetry, successfully circumvented controls in major AI models, with poetic prompts leading to a significantly higher rate of unsafe replies compared to prose.
\n", "date_published": "2025-12-02T20:46:00-04:00", "url": "https://kiledjian.com/2025/12/02/poetry-can-trick-ai-models.html", "tags": ["Artificial Intelligence","Cybersecurity \u0026 Privacy"] }, { "id": "http://ekiledjian2.micro.blog/2025/12/02/australia-abandons-proposed-mandatory-ai.html", "content_html": "Australia Abandons Proposed Mandatory AI Rules in New Plan
\nAustralia has shifted from proposed mandatory AI rules to a voluntary framework, opting for existing laws on privacy and copyright instead of new AI-specific legislation. This decision has been met with support from business groups but criticism from academics and the Greens, who argue it lacks enforcement and adequate investment compared to international approaches.
\n", "date_published": "2025-12-02T20:43:00-04:00", "url": "https://kiledjian.com/2025/12/02/australia-abandons-proposed-mandatory-ai.html", "tags": ["Artificial Intelligence","Technology \u0026 Business"] }, { "id": "http://ekiledjian2.micro.blog/2025/12/02/canada-launches-first-register-of.html", "content_html": "Canada launches first register of AI uses in federal government - Canada.ca
\nCanada has launched its first public AI Register to detail how artificial intelligence is used within the federal government, marking a key step in the public services AI Strategy. The register currently lists over400 AI systems across42 institutions and will undergo public consultations in2026 for refinement.
\n", "date_published": "2025-12-02T08:11:00-04:00", "url": "https://kiledjian.com/2025/12/02/canada-launches-first-register-of.html", "tags": ["Artificial Intelligence","Technology \u0026 Business"] }, { "id": "http://ekiledjian2.micro.blog/2025/11/28/improving-ai-outcomes-through-better.html", "title": "Improving AI Outcomes Through Better Prompting", "content_html": "AI is becoming integral to how many of us work, but too often the results still feel generic or misaligned. A small shift in how we prompt these systems can dramatically improve the quality, clarity and usefulness of their responses.
\nBy asking the AI to seek clarification before answering, we eliminate assumptions and get far stronger outputs.
Most people continue to use AI tools as if they were search engines: ask a question once and expect a complete answer. The challenge is that large language models are trained to fill gaps when faced with ambiguity. Research from the University of Washington and Stanford shows that when prompts lack detail, LLMs tend to infer the most likely meaning instead of checking for accuracy.
\nA simple adjustment solves this. Adding one line to a prompt—“Ask me clarifying questions until you are at least 95 per cent confident you understand what I need”—encourages the AI to slow down, confirm the context and deliver more precise results. It transforms a one-way query into a more thoughtful exchange.
\nThis technique has been validated across several domains. In customer support, a study involving more than five thousand agents found that clarification-capable AI increased resolved tickets per hour by up to 14 per cent. Broader enterprise research from McKinsey continues to show potential productivity gains of 30 to 45 per cent when generative AI is properly deployed within real workflows.
\nIn financial and legal settings, Bloomberg’s financial LLM improved accuracy in several reporting tasks by nearly 50 per cent when clarification routines were added. In software development, Adyen has documented stronger consistency and reduced rework by using AI that asks targeted questions before generating tests. Even in education, research shows that Socratic-style clarification leads to better critical thinking and stronger learning outcomes.
\nThe principle is simple: better questions drive better answers. When the AI verifies its understanding, it reduces misinterpretation, sharpens relevance and produces work that aligns more closely with what you actually need.
\nHere’s an easy example to try:
\n\n\n“Write a summary of this document. Before you begin, ask me any clarifying questions until you are 95 per cent confident you can complete this accurately.”
\n
Instead of guessing, the AI will ask about the intended audience, tone, length, purpose and constraints. Your responses give it the context required to produce something clear, accurate and fit for purpose.
\nAs we continue incorporating AI into our daily workflows, techniques like this ensure that the technology works with us, not around us. Encouraging the AI to ask questions first leads to outputs that are more thoughtful, more targeted and ultimately more useful.
\nIf you test this approach, I’d be interested in what you learn—successes, missteps and everything in between. The evolution of AI-assisted work is a shared journey, and the way we prompt these systems matters more than we think.
\n#ai #promptengineering #clarificationprompting #askbeforeanswer #generativeai #llm #aitools #aiproductivity #enterpriseai #futureofwork #digitaltransformation #aistrategy #consultativeai #aiworkflows #betterprompts #aiinnovation #leadership #technologyleadership #ciso #cybersecurity #datastrategy #intelligentautomation #knowledgework #aiinsights #aiadoption #smarterai #askbetterquestions #aiinbusiness #businessinnovation #thoughtleadership #aipractices #collaborativeai #thinkingassistants #worksmarter
\n![](\"https://cdn.uploads.micro.blog/255457/2025/chatgpt-image-nov-28-2025-at-11-13-44-am.png\")
",
"date_published": "2025-11-28T13:14:00-04:00",
"url": "https://kiledjian.com/2025/11/28/improving-ai-outcomes-through-better.html",
"tags": ["Artificial Intelligence"]
},
{
"id": "http://ekiledjian2.micro.blog/2025/11/05/comprehensive-analysis-of-leading-ai.html",
"title": "Comprehensive analysis of leading AI models in 2025: strengths, weaknesses and standout capabilities",
"content_html": "The artificial-intelligence landscape in 2025 has evolved into a highly competitive arena where numerous models offer distinct advantages for specific use cases. This article examines publicly available AI models shaping the industry, summarizing where each excels and where limitations remain.
\nChatGPT (GPT-5, GPT-4.5, GPT-4o): best generalist for agentic workflows, multi-step coding and polished consumer experiences
\nGrok 3/4 (xAI): strongest for real-time, web-aware analysis with extended reasoning and STEM tasks
\nClaude Sonnet 4.5 (Anthropic): leading coding model with hybrid reasoning and sustained autonomous operation claims
\nGemini 2.5 Pro (Google): native multimodality with ultra-long context (one to two million tokens) for cross-modal comprehension
\nKimi K2 (Moonshot): trillion-parameter mixture-of-experts model with strong coding claims and cost efficiency
\nQwen 3 235B (Alibaba): hybrid-reasoning with switchable thinking modes and extensive multilingual support
\nDeepSeek R1: open-reasoning model with transparent methodology and strong math and code performance
\nLlama 4 Maverick (Meta): natively multimodal open-weight model with favourable performance-to-cost ratio
\nMistral Large / Medium 3: efficient European multilingual model optimised for coding and pragmatic enterprise pricing
\nHermes 4 (Nous Research): open-weight hybrid reasoning with transparent thinking traces and minimal content restrictions
OpenAI maintains a multi-model strategy under the ChatGPT umbrella. GPT-4o is a multimodal generalist, GPT-4.5 emphasises conversational polish and GPT-5 is the flagship. The o-series (o1, o3, o3-mini) specialise in complex reasoning.
\nGPT-4o:
\nMultimodal input (text, image, voice) with near-human response times
\n128,000-token context window
\nImproved compute efficiency
\nStrong general-purpose performance
\nEnhanced vision capabilities
GPT-4.5:
\nMore natural conversational tone than GPT-4o
\nBetter sentiment detection and social-cue awareness
\nReduced hallucinations (~61.8 per cent to ~37.1 per cent)
\nSuitable for creative and nuanced writing
GPT-5:
\nReleased Aug. 7 2025
\nClaims state-of-the-art performance across coding, mathematics, writing and vision
\nMore unified operation with fast and deep-thinking modes
\nImproved reasoning for complex problem solving
O-series (o1, o3):
\nExcels at scientific, mathematical and coding-based reasoning
\nUses chain-of-thought logic to outperform GPT-4o on deep analyses
GPT-4o:
\nWeaker at abstract-reasoning, analogy, pattern recognition and spatial tasks
\nChallenges interpreting multi-speaker emotional nuance
\nStruggles with extended logic and very long code chains
GPT-4.5:
\nLess explicit step-by-step logic than o-series
\nNo default Voice Mode, video processing or screen-sharing
\nExpected retirement from the API July 2025
\nStill mis-reasons in some cases (for example, letter counting)
O-series:
\nSlower responses and higher cost
\nDoes not always express uncertainty
\nSome ChatGPT features unavailable in lower tiers
\nMessage caps in certain subscriptions
GPT-4o suits fast multimodal consumer interactions and creative content. GPT-4.5 fits creative writing, branding and emotionally nuanced tasks. GPT-5 supports complex engineering, agentic workflows and high-stakes problem solving. O-series models suit researchers, mathematicians and developers requiring explicit reasoning chains.
\nAnthropic’s Claude 4 family emphasises safer responses, long-context comprehension and strong coding performance. Sonnet 4.5 is promoted as the top coding model; Opus 4.1 focuses on advanced reasoning.
\nClaude Sonnet 4.5:
\nReported 77.2 per cent on SWE-bench Verified (82.0 per cent with high compute)
\n61.4 per cent on OSWorld for computer-use tasks
\nClaims of 30-plus hours of autonomous coding
\n100 per cent score on AIME 2025 using Python tools (87 per cent without)
\n83.4 per cent on GPQA Diamond
\nStrong alignment and low power-seeking behaviour
Claude Opus 4.1:
\nUp to 30 hours of autonomous operation
\nStrong multi-document and instruction following performance
\nBetter suited for analytical accuracy and specialised workflows
Sonnet 4.5:
\nMore cautious tone; sometimes over-hedges
\nVisual-reasoning (77.8 per cent MMMU) trails GPT-5 (84.2 per cent) and Gemini 2.5 Pro (82.0 per cent)
\nSafety classifiers can flag benign content
Opus 4.1:
\nRoughly five times the cost of Sonnet 4.5
\nInferior for software-development work
\nHigher latency
Sonnet 4.5 is strong for software development, debugging, testing and agent workflows. Opus 4.1 suits legal, finance and research tasks where accuracy justifies higher cost.
\nxAI, founded by Elon Musk, introduced Grok 3 in February 2025 and Grok 4 on July 9 2025. Both models emphasise long-context reasoning and real-time web-awareness through X.
\nGrok 3:
\nStrong on advanced math and STEM reasoning
\n128,000-token context window
\n“Think Mode” enables step-by-step reasoning
\n“DeepSearch” enables real-time content analysis
Grok 4:
\nAdds multi-agent reasoning
\nAvailable in a developer-focused subscription tier
\nMaintains long-context capability
Mixed output consistency
\nHigher hallucination risk than rivals
\nReal-time access is not universally guaranteed
\nPremium pricing for developer tiers
Advanced mathematics, STEM workflows, research leveraging real-time context and X-integrated environments.
\nGoogle DeepMind’s Gemini 2.5 Pro emphasises multimodal reasoning, ultra-long context and enterprise-ready integration.
\nStrong performance across math and science tasks
\nNative multimodal: text, image and video
\nUp to one million-token context, roadmap to two million
\nStrong cross-modal comprehension
Not as strong in coding or agentic workflows
\nSome reported factuality issues
\nBenchmarks for code remain mixed
Large-scale document analysis, multimedia reasoning and long-context enterprise workflows.
\nDeepSeek R1, launched January 2025, prioritises transparent reasoning, open licensing and efficiency.
\nOpen MIT licence
\nTransparent reasoning traces
\nStrong math and coding performance
\nEfficient 37 billion active parameter design
Shorter context window (~130,000)
\nPrimarily text-based; vision requires add-ons
\nWeaker usability and ecosystem support
Open-source deployments requiring transparent logic, math strength and flexible infrastructure.
\nMeta’s Llama 4 family arrived April 2025, featuring Scout and Maverick variants and providing multimodal capability in an open-weight model.
\nLlama 4 Maverick:
\nCompetitive performance with favourable cost
\nOpen-weight for on-prem or private-cloud deployment
\nMultimodal training across text, image and video
Llama 4 Scout:
\nClaims up to 10-million-token context
\nHigh cost-efficiency with fewer active parameters
Variant confusion (benchmarks on tuned vs released weights)
\nLicensing needed for 700-million-plus monthly active-user services
\nEcosystem still maturing
Maverick supports coding, enterprise document analysis, multilingual reasoning and cost-sensitive deployment. Scout suits ultra-long-context tasks.
\nQwen 3, released April 2025, targets hybrid reasoning, broad multilingual coverage and open developer frameworks.
\nSwitchable reasoning vs fast modes
\nSupport across 119 languages
\nApache 2.0 licensing
\nCompetitive math and code results
Earlier-stage ecosystem outside Asia
\nTool-use integrations less mature
\nArchitecture complexity adds integration overhead
Multilingual and open-source deployments, research requiring reasoning-depth control and flexible licensing.
\nMistral AI offers both open-weight and enterprise models. Medium 3 emphasises cost-efficiency; Mistral Large aims at enterprise reasoning.
\nMedium 3:
\nReported >90 per cent of Claude Sonnet 3.7 performance at far lower cost
\nFavourable pricing
\nDeployable across most clouds
\nStrong coding and STEM capability
Mistral Large:
\nEnterprise-grade multilingual support
\nNative function-calling and constrained output
\n32,000-token context
Creative writing trails specialist models
\nOccasional multi-step spatial-reasoning issues
\nLanguage variation by region
\nSome benchmarks favour rivals
Medium 3 fits cost-efficient enterprise coding and document understanding. Mistral Large suits multilingual enterprise deployments requiring more depth.
\nReleased August 2025, Hermes 4 prioritises hybrid reasoning, minimal content restriction and transparent output.
\nToggle between fast and step-wise reasoning
\nOpen-weight release with full reasoning traces
\nStrong reported math scores
\nLength-control methods reduce over-generation
High compute overhead for training and use
\nSmaller variants may overthink
\nMinimal filtering may not fit high-compliance industries
\nEcosystem less mature than major commercial models
Research, transparent reasoning pipelines and minimally censored open-source applied use.
\nBenchmark results are volatile and can depend on model variant, tuning, context length and test configuration. Many results are vendor-reported and lack broad third-party validation.
\nLong-context performance depends on endpoint and hardware. Reasoning modes can produce substantial performance swings. Open-weight models benefit from community scrutiny, whereas commercial models often publish fewer benchmarking details.
\nThe 2025 AI landscape provides exceptional choice.
\nGeneral-purpose chat: GPT-4o, GPT-4.5
\nEnterprise automation: GPT-5, Claude Sonnet 4.5, Grok 3
\nDeep reasoning: GPT-5, Claude Sonnet 4.5, Grok 3, Gemini 2.5 Pro
\nCoding excellence: Claude Sonnet 4.5, Kimi K2, GPT-5
\nCross-modal work: Gemini 2.5 Pro
\nUltra-long context: Gemini 2.5 Pro, Llama 4 Scout
\nCost optimisation: Llama 4 Maverick, Mistral Medium 3, Kimi K2
\nOpen-source and on-prem: DeepSeek R1, Qwen 3, Hermes 4, Llama 4, Kimi K2
\nAgentic workflows: Kimi K2, Claude Sonnet 4.5, Hermes 4
\nMultilingual: Qwen 3, Mistral Large
\nTransparent reasoning: Hermes 4, DeepSeek R1, Qwen 3
Selecting the right model depends on budget, deployment strategy, task complexity, transparency needs, regulatory requirements and integration demands. Ongoing evaluation remains critical as the market evolves rapidly.
\nThis analysis is for informational purposes only and reflects research available as of November 2025. No compensation influenced provider positioning. Capabilities, pricing and performance can change quickly. Readers should verify current information, especially for enterprise deployment, compliance, privacy and intellectual-property considerations.
\nLast updated November 2025
\nKeywords : #ArtificialIntelligence #AIModels #GPT5 #ClaudeSonnet45 #Gemini25Pro #Grok4 #DeepSeekR1 #KimiK2 #Qwen3 #Llama4 #MistralAI #Hermes4 #AgenticAI #AICoding #AIDevelopment #EnterpriseAI #GenerativeAI #MachineLearning #ML #MultimodalAI #OpenSourceAI #AIResearch #AITools #STEMAI #LongContextAI #AIComparison #TechInnovation #FutureOfAI #AIProductivity #AIEngineering #AITrends #AIin2025 #NeuralNetworks #AIAnalytics #BusinessAI #AIEcosystem
\n![](\"https://cdn.uploads.micro.blog/255457/2025/chatgpt-image-nov-5-2025-at-02-27-31-pm.png\")
",
"date_published": "2025-11-05T16:27:00-04:00",
"url": "https://kiledjian.com/2025/11/05/comprehensive-analysis-of-leading-ai.html",
"tags": ["Artificial Intelligence"]
},
{
"id": "http://ekiledjian2.micro.blog/2025/11/04/amazon-and-perplexity-have-kicked.html",
"content_html": "Amazon and Perplexity have kicked off the great AI web browser fight | The Verge
\nAmazon has requested that Perplexity stop its AI browser, Comet, from purchasing products on its site, accusing the AI startup of providing a degraded shopping experience. Perplexity, in turn, has accused Amazon of bullying and stated that the e-commerce giant is more interested in serving ads and sponsored results than facilitating easier shopping, despite Amazon’s CEO expecting future partnerships with AI shopping agents.
\n", "date_published": "2025-11-04T22:54:00-04:00", "url": "https://kiledjian.com/2025/11/04/amazon-and-perplexity-have-kicked.html", "tags": ["Artificial Intelligence","Technology \u0026 Business"] }, { "id": "http://ekiledjian2.micro.blog/2025/11/02/geoffrey-hinton-says-tech-giants.html", "content_html": "\nAccording to Geoffrey Hinton, tech giants cannot profit from their AI investments without replacing human labor. He believes that the massive capital expenditures by companies like Microsoft, Meta, and Alphabet are predicated on the idea of widespread job displacement by AI, though he acknowledges AI’s potential for good in fields like healthcare and education.
\n", "date_published": "2025-11-02T17:01:00-04:00", "url": "https://kiledjian.com/2025/11/02/geoffrey-hinton-says-tech-giants.html", "tags": ["Artificial Intelligence","Leadership \u0026 Mindset"] } ] }